{smcl}
{com}{sf}{ul off}{txt}{.-}
      name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\OneDrive - High Point University\Research\Mosul\Yazidi\Drones\TPV\Revision 1\Replication Instructions\TPV Soldiers Replication log file.smcl
  {txt}log type:  {res}smcl
 {txt}opened on:  {res}31 Oct 2025, 19:12:04

{com}. do "C:\Users\swhitt\OneDrive - High Point University\Research\Mosul\Yazidi\Drones\TPV\Revision 1\Replication Instructions\TPV Soldiers Replication do file.do"
{txt}
{com}. *Sacrificing Civilians to Save Soldiers: Public Support for Counterterrorism/Counterinsurgency Operations in Mosul and Basra, Iraq
. *Replication Instructions
. 
. *Sam Whitt, Vera Mironova, Douglas Page
. 
. *Below are instructions for replicating all manuscript and online appendix tables and figures in STATA using the dataset "TPV Soldiers Replication data.dta". Please contact Sam Whitt (swhitt@highpoint.edu) for questions regarding data replication. See also the dofile "TPV Soldiers Replication do file". 
. *Note: You may need to install STATA packages for the cibar, catcibar, and iebaltab commands. Use findit with the command name to identify and download the appropriate packets to install. 
. *"Stata user-generated commands to install for replication purposes"
. 
. *"cibar"
. ssc install cibar, replace
{txt}checking {hilite:cibar} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *"iebaltab from ietoolkit"
. ssc install ietoolkit, replace
{txt}checking {hilite:ietoolkit} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *"catcibar"
. net install catcibar, from("https://aarondwolf.github.io/catcibar") replace
checking {hilite:catcibar} consistency and verifying not already installed...
all files already exist and are up to date.
{txt}
{com}. 
. *regsensitivity
. ssc install regsensitivity, replace
{txt}checking {hilite:regsensitivity} consistency and verifying not already installed...
all files already exist and are up to date.

{com}. 
. *Manuscript Replication
. 
. *In text replication 
. 
. *The study was conducted between March 31-April 5, 2022 in Mosul and April 6-April 11 in Basra, with a sample size of 278 and 216, respectively 
. tab date if mosul==1

    {txt}Today’s {c |}
      Date  {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   1/4/2022 {c |}{res}         41       14.75       14.75
{txt}   2/4/2022 {c |}{res}         42       15.11       29.86
{txt}   3/4/2022 {c |}{res}         42       15.11       44.96
{txt}   4/4/2022 {c |}{res}         56       20.14       65.11
{txt}   5/4/2022 {c |}{res}         56       20.14       85.25
{txt}  31/3/2022 {c |}{res}         41       14.75      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        278      100.00
{txt}
{com}. tab date if mosul==0

    {txt}Today’s {c |}
      Date  {c |}      Freq.     Percent        Cum.
{hline 12}{c +}{hline 35}
   6/4/2022 {c |}{res}         22       10.19       10.19
{txt}   7/4/2022 {c |}{res}         22       10.19       20.37
{txt}   8/4/2022 {c |}{res}         43       19.91       40.28
{txt}   9/4/2022 {c |}{res}         43       19.91       60.19
{txt}  10/4/2022 {c |}{res}         43       19.91       80.09
{txt}  11/4/2022 {c |}{res}         43       19.91      100.00
{txt}{hline 12}{c +}{hline 35}
      Total {c |}{res}        216      100.00
{txt}
{com}. 
. *We simplify the analysis by combining all four items into a common continuous interim covariance index ranging from 1=strongly oppose to 4=strongly agree (Cronbach's alpha=0.87). 
. alpha revdrones revpilots revshelling revdeploysoldiers

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .2873535
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.8664
{txt}
{com}. 
. *The effect size of the Sacrificing Civilians treatment relative to control is moderate to small (Cohen's d = 0.23), while the effect size of the Saving Soldiers treatment is relatively large (Cohen's d  = 0.51). 
. esize twosample alphactoindex if militarytxt~=2, by(militarytxt) unequal

{txt}Effect size based on mean comparison, unequal variances

                               Obs per group:
                   Sacrificing civilians txt =        162
                           Control, no prime =        168
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}  .232827{col 34}{space 3} .0160949{col 46}{space 3} .4492059
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2} .2322941{col 34}{space 3} .0160581{col 46}{space 3} .4481778
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
            Satterthwaite's degrees of freedom ={col 51}{res}327.4459
{txt}
{com}. esize twosample alphactoindex if militarytxt~=1, by(militarytxt) unequal

{txt}Effect size based on mean comparison, unequal variances

                               Obs per group:
                           Save soldiers txt =        156
                           Control, no prime =        168
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2} .5083418{col 34}{space 3} .2865232{col 46}{space 3} .7293937
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2} .5071567{col 34}{space 3} .2858552{col 46}{space 3} .7276932
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
            Satterthwaite's degrees of freedom ={col 51}{res}321.4190
{txt}
{com}. 
. *…the Saving Soldiers treatment effect is significant when compared to the Civilian Casualty treatment as a placebo control with a moderate effect size (Cohen's d = 0.28). 
. esize twosample alphactoindex, by(civsoldiertxt) unequal

{txt}Effect size based on mean comparison, unequal variances

                               Obs per group:
                   Sacrificing civilians txt =        162
                          Saving soldier txt =        156
{res}{col 1}{text}{hline 20}{c TT}{hline 12}{hline 12}{hline 12}
{col 1}{text}        Effect size{col 21}{c |}   Estimate{col 34}    [95% conf. interval]
{res}{col 1}{text}{hline 20}{c +}{hline 12}{hline 12}{hline 12}
{col 1}{text}          Cohen's {it:d}{col 21}{c |}{result}{space 2}-.2838161{col 34}{space 3}-.5045613{col 46}{space 3}-.0626262
{col 1}{text}         Hedges's {it:g}{col 21}{c |}{result}{space 2}-.2831418{col 34}{space 3}-.5033627{col 46}{space 3}-.0624774
{col 1}{text}{hline 20}{c BT}{hline 12}{hline 12}{hline 12}
            Satterthwaite's degrees of freedom ={col 51}{res}315.9993
{txt}
{com}. 
. *First, we measure trust in the military using the question How much do you trust the Iraqi Armed Forces to do what's best for the country? Response options range on a four-point scale from 1=no trust at all to 4=a great deal, and trust was higher in Mosul (93.5% have a great deal of trust) than in Basra (35.4%). 
. tab revtrustarmy if mosul==1

    {txt}(revcode) {c |}
  trust Iraqi {c |}
 Armed Forces {c |}      Freq.     Percent        Cum.
{hline 14}{c +}{hline 35}
not very much {c |}{res}          2        0.72        0.72
{txt}a fair amount {c |}{res}         16        5.76        6.47
{txt} a great deal {c |}{res}        260       93.53      100.00
{txt}{hline 14}{c +}{hline 35}
        Total {c |}{res}        278      100.00
{txt}
{com}. tab revtrustarmy if mosul==0

    {txt}(revcode) {c |}
  trust Iraqi {c |}
 Armed Forces {c |}      Freq.     Percent        Cum.
{hline 14}{c +}{hline 35}
not very much {c |}{res}         17        7.91        7.91
{txt}a fair amount {c |}{res}        122       56.74       64.65
{txt} a great deal {c |}{res}         76       35.35      100.00
{txt}{hline 14}{c +}{hline 35}
        Total {c |}{res}        215      100.00
{txt}
{com}. 
. *Only 3% of people in Mosul and 15% in Basra felt that most people supported ISIS; the vast majority in both Mosul (87%) and Basra (84%) thought that some people supported ISIS, but not a majority. 
. tab supportisis if mosul==1

{txt}How many people in Mosul do you {c |}
          think supported ISIS  {c |}      Freq.     Percent        Cum.
{hline 32}{c +}{hline 35}
                almost everyone {c |}{res}          6        2.19        2.19
{txt}                    most people {c |}{res}          2        0.73        2.92
{txt}some people, but not a majority {c |}{res}        239       87.23       90.15
{txt}           almost no one at all {c |}{res}         27        9.85      100.00
{txt}{hline 32}{c +}{hline 35}
                          Total {c |}{res}        274      100.00
{txt}
{com}. tab supportisis if mosul==0

{txt}How many people in Mosul do you {c |}
          think supported ISIS  {c |}      Freq.     Percent        Cum.
{hline 32}{c +}{hline 35}
                    most people {c |}{res}         32       15.02       15.02
{txt}some people, but not a majority {c |}{res}        179       84.04       99.06
{txt}           almost no one at all {c |}{res}          2        0.94      100.00
{txt}{hline 32}{c +}{hline 35}
                          Total {c |}{res}        213      100.00
{txt}
{com}. 
. *Using a composite interim covariance index (see online appendix for details), we find that Mosul residents showed greater concern about protecting others from harm (unpaired t-test = 13.3, p<0.000) than Basra, providing a potential distancing explanation for greater support for CTO/COIN in Mosul than in Basra. 
. ttest revalphaprotect, by(mosul) unpaired unequal

{txt}Two-sample t test with unequal variances
{hline 9}{c TT}{hline 68}
   Group{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
       0 {c |}{res}{col 12}    216{col 22} 3.029541{col 34} .0369757{col 46} .5434297{col 58}  2.95666{col 70} 3.102423
       {txt}1 {c |}{res}{col 12}    278{col 22} 3.577938{col 34} .0180297{col 46} .3006157{col 58} 3.542445{col 70}  3.61343
{txt}{hline 9}{c +}{hline 68}
Combined {c |}{res}{col 12}    494{col 22} 3.338153{col 34} .0226626{col 46} .5037013{col 58} 3.293626{col 70}  3.38268
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 22}-.5483962{col 34} .0411373{col 58}-.6293342{col 70}-.4674582
{txt}{hline 9}{c BT}{hline 68}
    diff = mean({res}0{txt}) - mean({res}1{txt})                                      t = {res}-13.3309
{txt}H0: diff = 0                     Satterthwaite's degrees of freedom = {res} 315.547

    {txt}Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = {res}0.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}1.0000
{txt}
{com}. 
. *We found that everyone in the Mosul sample experienced at least 1 form of the above victimization, while 71% suffered two and 22% endured three forms of victimization.
. 
. *Code to generate additive ISIS victim index (already generated)
. *gen addisisvictimall = punishedisis + fampunishedisis + injuredisis + faminjuredisis + famkilledisis + imprisonedisis + fleehomeisis + homedamagedisis + womenabusedisis + injuredlib + faminjuredlib + famkilledlib + homedamagedlib + imprisonedlib + fledhomelib + womenabusedlib + dsexassault
. *tab addisisvictimall
. *gen addisisvictim3 = addisisvictimall
. *replace addisisvictim3  = 3 if addisisvictim3 >3
. *tab addisisvictim3 if mosul==1
. 
. *The average tolerance score in Mosul was 8.88 (SD=0.13) compared to 4.02 (SD=0.14) in Basra. 
. sum revdeathsacceptable if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revdeathsa~e {c |}{res}        196    8.882653    1.889649          2         10
{txt}
{com}. sum revdeathsacceptable if mosul==0

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revdeathsa~e {c |}{res}        120    4.016667    1.560992          0          9
{txt}
{com}. 
. *In Basra, only 5.4% indicated probably not, while 51.4% indicated probably yes, and only 43.2% thought that the use of advanced weaponry definitely reduced soldier deaths. In Mosul, 65.3% stated probably not, 29.7% probably yes, and only 5.1% definitely yes. 
. tab lesssoldierdeaths if mosul==0

{txt}Likely reduced {c |}
 the number of {c |}
      soldiers {c |}
 killed during {c |}
the operation? {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
definitely yes {c |}{res}         16       43.24       43.24
{txt}  probably yes {c |}{res}         19       51.35       94.59
{txt}   probably no {c |}{res}          2        5.41      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}         37      100.00
{txt}
{com}. tab lesssoldierdeaths if mosul==1

{txt}Likely reduced {c |}
 the number of {c |}
      soldiers {c |}
 killed during {c |}
the operation? {c |}      Freq.     Percent        Cum.
{hline 15}{c +}{hline 35}
definitely yes {c |}{res}          6        5.08        5.08
{txt}  probably yes {c |}{res}         35       29.66       34.75
{txt}   probably no {c |}{res}         77       65.25      100.00
{txt}{hline 15}{c +}{hline 35}
         Total {c |}{res}        118      100.00
{txt}
{com}. 
. *Manuscript Tables and Figures
. 
. *Figure 2. Support for CTO/COIN
. 
. *Figure 2a.
. 
. catcibar revdrones revpilot revshelling revdeploysoldiers, over(mosul)
{txt}
{com}. 
. *Note: additional formatting required
. 
. *Figure 2b. 
. 
. cibar alphactoindex, over1(militarytxt0)
{res}{txt}
{com}. 
. *Note: additional formatting required
. 
. *Figure 3 Tolerance of civilian casualties 
. 
. *Figure 3a. Histograms
. 
. histogram revdeathsacceptable if mosul==0, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}0{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g1
{res}{txt}file {bf:g1.gph} saved

{com}. histogram revdeathsacceptable if mosul==1, discrete percent addlabels addlabopts(mlabformat(%2.1f))
{txt}(start={res}2{txt}, width={res}1{txt})
{res}{txt}
{com}. graph save g2
{res}{txt}file {bf:g2.gph} saved

{com}. graph combine "g1.gph" "g2.gph"
{res}{txt}
{com}. *Note: additional formatting required using 
. 
. *Figure 3b. ATEs
. 
. cibar revdeathsacceptable, over1(civsoldiertxt)
{res}{txt}
{com}. cibar revdeathsacceptable, over1(civsoldiertxt) over2(mosul)
{res}{txt}
{com}. 
. *Figure 4. CTO/COIN support treatment compliance
. 
. cibar alphactoindex, over1(lesssoldierdeaths)
{res}{txt}
{com}. graph save g3
{res}{txt}file {bf:g3.gph} saved

{com}. cibar revdeathsacceptable, over1(lesssoldierdeaths)
{res}{txt}
{com}. graph save g4
{res}{txt}file {bf:g4.gph} saved

{com}. graph combine "g3.gph" "g4.gph"
{res}{txt}
{com}. *Note: additional formatting required  
. 
. *Table 1. Sample Summary Statistics
. 
. sum female age professional laborer unemployed westmosul  if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}female {c |}{res}        278    .1510791    .3587719          0          1
{txt}{space 9}age {c |}{res}        278    33.96043    13.88615         18         84
{txt}professional {c |}{res}        278     .528777    .5000714          0          1
{txt}{space 5}laborer {c |}{res}        278    .0935252    .2916921          0          1
{txt}{space 2}unemployed {c |}{res}        278    .1043165     .306222          0          1
{txt}{hline 13}{c +}{hline 57}
{space 3}westmosul {c |}{res}        278    .3489209    .4774885          0          1
{txt}
{com}. sum female age professional laborer unemployed westmosul  if mosul==0

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}female {c |}{res}        216    .3425926    .4756789          0          1
{txt}{space 9}age {c |}{res}        216    30.11574    8.130364         19         55
{txt}professional {c |}{res}        216         .25    .4340185          0          1
{txt}{space 5}laborer {c |}{res}        216    .1111111    .3149997          0          1
{txt}{space 2}unemployed {c |}{res}        216    .1574074    .3650304          0          1
{txt}{hline 13}{c +}{hline 57}
{space 3}westmosul {c |}{res}          0
{txt}
{com}. 
. *Table 2.  Support for CTO/COIN (OLS Regression) 
. 
. reg alphactoindex ib3.militarytxt, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(2, 483)         =  {res}    10.62
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0421
                                                {txt}Root MSE          =    {res} .56644

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .1351411{col 27}{space 2} .0638279{col 38}{space 1}    2.12{col 47}{space 3}0.035{col 55}{space 4} .0097265{col 68}{space 3} .2605557
{txt}Save soldi..  {c |}{col 15}{res}{space 2}  .290293{col 27}{space 2} .0632207{col 38}{space 1}    4.59{col 47}{space 3}0.000{col 55}{space 4} .1660714{col 68}{space 3} .4145147
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 3.259921{col 27}{space 2} .0464572{col 38}{space 1}   70.17{col 47}{space 3}0.000{col 55}{space 4} 3.168637{col 68}{space 3} 3.351204
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex civsoldiertxt, robust

{txt}Linear regression                               Number of obs     = {res}       318
                                                {txt}F(1, 316)         =  {res}     6.41
                                                {txt}Prob > F          = {res}    0.0118
                                                {txt}R-squared         = {res}    0.0199
                                                {txt}Root MSE          =    {res} .54666

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2} .1551519{col 27}{space 2} .0612761{col 38}{space 1}    2.53{col 47}{space 3}0.012{col 55}{space 4} .0345912{col 68}{space 3} .2757127
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.395062{col 27}{space 2} .0437716{col 38}{space 1}   77.56{col 47}{space 3}0.000{col 55}{space 4} 3.308941{col 68}{space 3} 3.481182
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt mosul, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(3, 482)         =  {res}   297.73
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6375
                                                {txt}Root MSE          =    {res} .34883

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .1071772{col 27}{space 2} .0417177{col 38}{space 1}    2.57{col 47}{space 3}0.010{col 55}{space 4} .0252061{col 68}{space 3} .1891482
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .0790204{col 27}{space 2} .0391545{col 38}{space 1}    2.02{col 47}{space 3}0.044{col 55}{space 4} .0020858{col 68}{space 3} .1559549
{txt}{space 13} {c |}
{space 8}mosul {c |}{col 15}{res}{space 2} .9191624{col 27}{space 2}  .033401{col 38}{space 1}   27.52{col 47}{space 3}0.000{col 55}{space 4} .8535328{col 68}{space 3} .9847921
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.811282{col 27}{space 2} .0325514{col 38}{space 1}   86.36{col 47}{space 3}0.000{col 55}{space 4} 2.747322{col 68}{space 3} 2.875242
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex mosul ib3.militarytxt revtrustarmy supportisis revalphaprotect addisisvictim3 female age education unemployed sunni arab , robust

{txt}Linear regression                               Number of obs     = {res}       477
                                                {txt}F(13, 463)        =  {res}    94.15
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6852
                                                {txt}Root MSE          =    {res} .32968

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}mosul {c |}{col 15}{res}{space 2} .8977523{col 27}{space 2} .0873513{col 38}{space 1}   10.28{col 47}{space 3}0.000{col 55}{space 4} .7260982{col 68}{space 3} 1.069406
{txt}{space 13} {c |}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .0930639{col 27}{space 2}  .041929{col 38}{space 1}    2.22{col 47}{space 3}0.027{col 55}{space 4} .0106691{col 68}{space 3} .1754586
{txt}Save soldi..  {c |}{col 15}{res}{space 2}  .041578{col 27}{space 2} .0383383{col 38}{space 1}    1.08{col 47}{space 3}0.279{col 55}{space 4}-.0337607{col 68}{space 3} .1169166
{txt}{space 13} {c |}
{space 1}revtrustarmy {c |}{col 15}{res}{space 2} .1508719{col 27}{space 2} .0418776{col 38}{space 1}    3.60{col 47}{space 3}0.000{col 55}{space 4} .0685781{col 68}{space 3} .2331657
{txt}{space 2}supportisis {c |}{col 15}{res}{space 2}-.0023417{col 27}{space 2} .0399352{col 38}{space 1}   -0.06{col 47}{space 3}0.953{col 55}{space 4}-.0808184{col 68}{space 3} .0761351
{txt}revalphapro~t {c |}{col 15}{res}{space 2}  .064844{col 27}{space 2} .0388979{col 38}{space 1}    1.67{col 47}{space 3}0.096{col 55}{space 4}-.0115944{col 68}{space 3} .1412823
{txt}addisisvict~3 {c |}{col 15}{res}{space 2}-.1016247{col 27}{space 2} .0405106{col 38}{space 1}   -2.51{col 47}{space 3}0.012{col 55}{space 4}-.1812321{col 68}{space 3}-.0220172
{txt}{space 7}female {c |}{col 15}{res}{space 2}-.0370559{col 27}{space 2} .0466829{col 38}{space 1}   -0.79{col 47}{space 3}0.428{col 55}{space 4}-.1287924{col 68}{space 3} .0546807
{txt}{space 10}age {c |}{col 15}{res}{space 2} .0049373{col 27}{space 2} .0010289{col 38}{space 1}    4.80{col 47}{space 3}0.000{col 55}{space 4} .0029154{col 68}{space 3} .0069592
{txt}{space 4}education {c |}{col 15}{res}{space 2} .0112997{col 27}{space 2} .0206419{col 38}{space 1}    0.55{col 47}{space 3}0.584{col 55}{space 4}-.0292636{col 68}{space 3} .0518631
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2} .0735524{col 27}{space 2} .0569709{col 38}{space 1}    1.29{col 47}{space 3}0.197{col 55}{space 4}-.0384012{col 68}{space 3} .1855059
{txt}{space 8}sunni {c |}{col 15}{res}{space 2} .1380536{col 27}{space 2} .0513247{col 38}{space 1}    2.69{col 47}{space 3}0.007{col 55}{space 4} .0371953{col 68}{space 3} .2389119
{txt}{space 9}arab {c |}{col 15}{res}{space 2} .0502689{col 27}{space 2} .0920276{col 38}{space 1}    0.55{col 47}{space 3}0.585{col 55}{space 4}-.1305747{col 68}{space 3} .2311124
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.980046{col 27}{space 2} .2494374{col 38}{space 1}    7.94{col 47}{space 3}0.000{col 55}{space 4} 1.489877{col 68}{space 3} 2.470216
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Table 3. Tolerance of civilian casualties (OLS regression)
. 
. reg revdeathsacceptable civsoldiertxt, robust

{txt}Linear regression                               Number of obs     = {res}       316
                                                {txt}F(1, 314)         =  {res}    28.30
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0823
                                                {txt}Root MSE          =    {res} 2.8343

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2} 1.692721{col 27}{space 2} .3181768{col 38}{space 1}    5.32{col 47}{space 3}0.000{col 55}{space 4} 1.066693{col 68}{space 3} 2.318749
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.209877{col 27}{space 2} .2332567{col 38}{space 1}   26.62{col 47}{space 3}0.000{col 55}{space 4} 5.750933{col 68}{space 3}  6.66882
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revdeathsacceptable civsoldiertxt mosul, robust

{txt}Linear regression                               Number of obs     = {res}       316
                                                {txt}F(2, 313)         =  {res}   470.77
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6550
                                                {txt}Root MSE          =    {res} 1.7406

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2} .7097325{col 27}{space 2} .2215522{col 38}{space 1}    3.20{col 47}{space 3}0.001{col 55}{space 4} .2738125{col 68}{space 3} 1.145652
{txt}{space 8}mosul {c |}{col 15}{res}{space 2} 4.708831{col 27}{space 2} .2209392{col 38}{space 1}   21.31{col 47}{space 3}0.000{col 55}{space 4} 4.274118{col 68}{space 3} 5.143545
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  3.76826{col 27}{space 2} .1398516{col 38}{space 1}   26.94{col 47}{space 3}0.000{col 55}{space 4} 3.493092{col 68}{space 3} 4.043428
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex civsoldiertxt, robust

{txt}Linear regression                               Number of obs     = {res}       318
                                                {txt}F(1, 316)         =  {res}     6.41
                                                {txt}Prob > F          = {res}    0.0118
                                                {txt}R-squared         = {res}    0.0199
                                                {txt}Root MSE          =    {res} .54666

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2} .1551519{col 27}{space 2} .0612761{col 38}{space 1}    2.53{col 47}{space 3}0.012{col 55}{space 4} .0345912{col 68}{space 3} .2757127
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 3.395062{col 27}{space 2} .0437716{col 38}{space 1}   77.56{col 47}{space 3}0.000{col 55}{space 4} 3.308941{col 68}{space 3} 3.481182
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex civsoldiertxt revdeathsacceptable, robust

{txt}Linear regression                               Number of obs     = {res}       316
                                                {txt}F(2, 313)         =  {res}   131.03
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4487
                                                {txt}Root MSE          =    {res} .41035

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2}-.0512945{col 27}{space 2} .0455978{col 38}{space 1}   -1.12{col 47}{space 3}0.261{col 55}{space 4}-.1410114{col 68}{space 3} .0384224
{txt}revdeathsac~e {c |}{col 15}{res}{space 2} .1271417{col 27}{space 2} .0078641{col 38}{space 1}   16.17{col 47}{space 3}0.000{col 55}{space 4} .1116686{col 68}{space 3} .1426148
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.605527{col 27}{space 2} .0696934{col 38}{space 1}   37.39{col 47}{space 3}0.000{col 55}{space 4} 2.468401{col 68}{space 3} 2.742654
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex civsoldiertxt revdeathsacceptable mosul revtrustarmy, robust

{txt}Linear regression                               Number of obs     = {res}       315
                                                {txt}F(4, 310)         =  {res}   142.63
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6838
                                                {txt}Root MSE          =    {res} .31141

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2}-.0177747{col 27}{space 2} .0348485{col 38}{space 1}   -0.51{col 47}{space 3}0.610{col 55}{space 4}-.0863443{col 68}{space 3} .0507949
{txt}revdeathsac~e {c |}{col 15}{res}{space 2} .0198669{col 27}{space 2} .0089014{col 38}{space 1}    2.23{col 47}{space 3}0.026{col 55}{space 4} .0023521{col 68}{space 3} .0373816
{txt}{space 8}mosul {c |}{col 15}{res}{space 2} .6799855{col 27}{space 2} .0675407{col 38}{space 1}   10.07{col 47}{space 3}0.000{col 55}{space 4} .5470894{col 68}{space 3} .8128816
{txt}{space 1}revtrustarmy {c |}{col 15}{res}{space 2} .2459927{col 27}{space 2}  .050094{col 38}{space 1}    4.91{col 47}{space 3}0.000{col 55}{space 4} .1474254{col 68}{space 3} .3445599
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.250978{col 27}{space 2} .1399239{col 38}{space 1}   16.09{col 47}{space 3}0.000{col 55}{space 4} 1.975657{col 68}{space 3} 2.526298
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Online Appendix Replication
. 
. *Sample Summary Statistics
. 
. sum female age professional laborer unemployed westmosul  if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}female {c |}{res}        278    .1510791    .3587719          0          1
{txt}{space 9}age {c |}{res}        278    33.96043    13.88615         18         84
{txt}professional {c |}{res}        278     .528777    .5000714          0          1
{txt}{space 5}laborer {c |}{res}        278    .0935252    .2916921          0          1
{txt}{space 2}unemployed {c |}{res}        278    .1043165     .306222          0          1
{txt}{hline 13}{c +}{hline 57}
{space 3}westmosul {c |}{res}        278    .3489209    .4774885          0          1
{txt}
{com}. sum female age professional laborer unemployed westmosul  if mosul==0

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
{space 6}female {c |}{res}        216    .3425926    .4756789          0          1
{txt}{space 9}age {c |}{res}        216    30.11574    8.130364         19         55
{txt}professional {c |}{res}        216         .25    .4340185          0          1
{txt}{space 5}laborer {c |}{res}        216    .1111111    .3149997          0          1
{txt}{space 2}unemployed {c |}{res}        216    .1574074    .3650304          0          1
{txt}{hline 13}{c +}{hline 57}
{space 3}westmosul {c |}{res}          0
{txt}
{com}. 
. *Summary Statistics-Mosul
. 
. sum revprotectsunni-revprotectgay revalphaprotect supportisis punishedisis dsexassault fampunishedisis faminjuredisis famkilledisis womenabusedisis fleehomeisis homedamagedisis injuredlib faminjuredlib famkilledlib homedamagedlib imprisonedlib fledhomelib homelootedlib  female age education professional laborer unemployed westmosul  if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revprotec~ni {c |}{res}        278    3.935252    .2465248          3          4
{txt}revprotect~a {c |}{res}        278     3.92446    .2647366          3          4
{txt}revprotec~di {c |}{res}        276    3.916667    .2768875          3          4
{txt}revprotect~d {c |}{res}        276     3.90942    .3118013          2          4
{txt}revprotec~an {c |}{res}        278    3.899281    .3014996          3          4
{txt}{hline 13}{c +}{hline 57}
revprotec~gn {c |}{res}        274     3.20438    .7426187          1          4
{txt}revprotect~y {c |}{res}        272    2.235294    .7942161          1          4
{txt}revalphapr~t {c |}{res}        278    3.577938    .3006157   2.285714          4
{txt}{space 1}supportisis {c |}{res}        274    3.047445     .438041          1          4
{txt}punishedisis {c |}{res}        278    .1834532    .3877357          0          1
{txt}{hline 13}{c +}{hline 57}
{space 1}dsexassault {c |}{res}        278     .057554    .2333181          0          1
{txt}fampunishe~s {c |}{res}        278    .0431655     .203596          0          1
{txt}faminjured~s {c |}{res}        278    .0683453    .2527926          0          1
{txt}famkilledi~s {c |}{res}        278    .0179856    .1331386          0          1
{txt}womenabuse~s {c |}{res}        278    .3884892    .4882858          0          1
{txt}{hline 13}{c +}{hline 57}
fleehomeisis {c |}{res}        278    .0971223    .2966583          0          1
{txt}homedamage~s {c |}{res}        278    .1978417    .3990906          0          1
{txt}{space 2}injuredlib {c |}{res}        278     .028777    .1674806          0          1
{txt}faminjured~b {c |}{res}        278    .0647482    .2465248          0          1
{txt}famkilledlib {c |}{res}        278    .0143885    .1193007          0          1
{txt}{hline 13}{c +}{hline 57}
homedamage~b {c |}{res}        278    .0503597    .2190805          0          1
{txt}imprisoned~b {c |}{res}        278    .1402878    .3479116          0          1
{txt}{space 1}fledhomelib {c |}{res}        278    .0791367    .2704388          0          1
{txt}homelooted~b {c |}{res}        278    .2014388    .4017984          0          1
{txt}{space 6}female {c |}{res}        278    .1510791    .3587719          0          1
{txt}{hline 13}{c +}{hline 57}
{space 9}age {c |}{res}        278    33.96043    13.88615         18         84
{txt}{space 3}education {c |}{res}        278    3.460432    .6772869          1          4
{txt}professional {c |}{res}        278     .528777    .5000714          0          1
{txt}{space 5}laborer {c |}{res}        278    .0935252    .2916921          0          1
{txt}{space 2}unemployed {c |}{res}        278    .1043165     .306222          0          1
{txt}{hline 13}{c +}{hline 57}
{space 3}westmosul {c |}{res}        278    .3489209    .4774885          0          1
{txt}
{com}. 
. *Protection from Harm Index Construction 
. 
. alpha revprotectsunni-revprotectgay

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .2158706
{txt}Number of items in the scale:{col 34}{res}        7
{txt}Scale reliability coefficient:{col 34}{res}   0.8561
{txt}
{com}. factor revprotectsunni-revprotectgay
{txt}(obs=480)

Factor analysis/correlation{col 50}Number of obs    = {res}       480
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       3
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}      18

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      3.66557      3.01540            0.9260       0.9260
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.65017      0.55513            0.1643       1.0903
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}      0.09504      0.13916            0.0240       1.1143
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.04412      0.03657           -0.0111       1.1032
{txt}{col 5}{ralign 11:Factor5}  {c |}{res}     -0.08069      0.05235           -0.0204       1.0828
{txt}{col 5}{ralign 11:Factor6}  {c |}{res}     -0.13304      0.06155           -0.0336       1.0492
{txt}{col 5}{ralign 11:Factor7}  {c |}{res}     -0.19459            .           -0.0492       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}21{txt}) ={res} 1889.26{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{space 1}{ralign 8:Factor3}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:revprotec~ni}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.5539}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.2298}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1982}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.6010}}}{space 1}
{space 4}{space 0}{ralign 12:revprotect~a}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8074}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1821}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1277}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2986}}}{space 1}
{space 4}{space 0}{ralign 12:revprotec~di}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8198}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1485}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0616}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3020}}}{space 1}
{space 4}{space 0}{ralign 12:revprotect~d}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8129}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1699}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0864}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3028}}}{space 1}
{space 4}{space 0}{ralign 12:revprotec~an}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8182}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0318}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1558}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3053}}}{space 1}
{space 4}{space 0}{ralign 12:revprotec~gn}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.7212}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.4414}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0125}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2849}}}{space 1}
{space 4}{space 0}{ralign 12:revprotect~y}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.4293}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.5634}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0612}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4945}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. kdensity revalphaprotect
{res}{txt}
{com}. 
. *CTO/COIN Index Construction 
. 
. alpha revdrones revpilots revshelling revdeploysoldiers

{txt}Test scale = mean(unstandardized items)

Average interitem covariance:{col 34}{res} .2873535
{txt}Number of items in the scale:{col 34}{res}        4
{txt}Scale reliability coefficient:{col 34}{res}   0.8664
{txt}
{com}. factor revdrones revpilots revshelling revdeploysoldiers
{txt}(obs=482)

Factor analysis/correlation{col 50}Number of obs    = {res}       482
{col 5}{txt}Method: principal factors{col 50}Retained factors =   {res}       2
{col 5}{txt}Rotation: (unrotated){col 50}Number of params =   {res}       6

{txt}{col 5}{hline 13}{c TT}{hline 60}
{col 5}     Factor  {c |} {ralign 12:Eigenvalue}   Difference        Proportion   Cumulative
{col 5}{hline 13}{c +}{hline 60}
{col 5}{ralign 11:Factor1}  {c |}{res}      2.53816      2.47604            1.0856       1.0856
{txt}{col 5}{ralign 11:Factor2}  {c |}{res}      0.06212      0.18199            0.0266       1.1122
{txt}{col 5}{ralign 11:Factor3}  {c |}{res}     -0.11987      0.02248           -0.0513       1.0609
{txt}{col 5}{ralign 11:Factor4}  {c |}{res}     -0.14236            .           -0.0609       1.0000
{txt}{col 5}{hline 13}{c BT}{hline 60}
{col 5}LR test: independent vs. saturated:  chi2({res}6{txt})  ={res} 1076.20{txt} Prob>chi2 ={res} 0.0000

{txt}Factor loadings (pattern matrix) and unique variances

{space 4}{hline 13}{c  TT}{hline 10}{hline 10}{c  TT}{hline 14}
{space 4}{space 0}{ralign 12:Variable}{space 1}{c |}{space 1}{ralign 8:Factor1}{space 1}{space 1}{ralign 8:Factor2}{space 1}{c |}{space 1}{ralign 12:Uniqueness}{space 1}
{space 4}{hline 13}{c   +}{hline 10}{hline 10}{c   +}{hline 14}
{space 4}{space 0}{ralign 12:revdrones}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8532}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.0920}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.2636}}}{space 1}
{space 4}{space 0}{ralign 12:revpilots}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8115}}}{space 1}{space 1}{ralign 8:{res:{sf: -0.1366}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3228}}}{space 1}
{space 4}{space 0}{ralign 12:revshelling}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.8225}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.0951}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.3145}}}{space 1}
{space 4}{space 0}{ralign 12:revdeploys~s}{space 1}{c |}{space 1}{ralign 8:{res:{sf:  0.6894}}}{space 1}{space 1}{ralign 8:{res:{sf:  0.1611}}}{space 1}{c |}{space 1}{center 12:{res:{sf:    0.4988}}}{space 1}
{space 4}{hline 13}{c  BT}{hline 10}{hline 10}{c  BT}{hline 14}

{com}. 
. *Experimental Robustness Checks
. 
. *The Table below shows how the treatment effects are consistent across index components using OLS Regression.
. reg revdrones ib3.militarytxt, robust

{txt}Linear regression                               Number of obs     = {res}       485
                                                {txt}F(2, 482)         =  {res}     9.03
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0384
                                                {txt}Root MSE          =    {res} .58716

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}    revdrones{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .1746032{col 27}{space 2} .0684025{col 38}{space 1}    2.55{col 47}{space 3}0.011{col 55}{space 4} .0401993{col 68}{space 3} .3090071
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .2835637{col 27}{space 2}  .066733{col 38}{space 1}    4.25{col 47}{space 3}0.000{col 55}{space 4} .1524402{col 68}{space 3} .4146873
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 3.380952{col 27}{space 2} .0532778{col 38}{space 1}   63.46{col 47}{space 3}0.000{col 55}{space 4} 3.276267{col 68}{space 3} 3.485638
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revpilots ib3.militarytxt, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(2, 483)         =  {res}    16.01
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0658
                                                {txt}Root MSE          =    {res} .62326

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}    revpilots{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .3174603{col 27}{space 2} .0724676{col 38}{space 1}    4.38{col 47}{space 3}0.000{col 55}{space 4} .1750697{col 68}{space 3} .4598509
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .3708791{col 27}{space 2} .0682967{col 38}{space 1}    5.43{col 47}{space 3}0.000{col 55}{space 4} .2366838{col 68}{space 3} .5050745
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 3.238095{col 27}{space 2} .0528937{col 38}{space 1}   61.22{col 47}{space 3}0.000{col 55}{space 4} 3.134165{col 68}{space 3} 3.342025
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revshelling ib3.militarytxt, robust

{txt}Linear regression                               Number of obs     = {res}       484
                                                {txt}F(2, 481)         =  {res}     7.28
                                                {txt}Prob > F          = {res}    0.0008
                                                {txt}R-squared         = {res}    0.0285
                                                {txt}Root MSE          =    {res} .69833

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}  revshelling{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .0800238{col 27}{space 2} .0787045{col 38}{space 1}    1.02{col 47}{space 3}0.310{col 55}{space 4}-.0746234{col 68}{space 3}  .234671
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .2846772{col 27}{space 2} .0774747{col 38}{space 1}    3.67{col 47}{space 3}0.000{col 55}{space 4} .1324465{col 68}{space 3} .4369079
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 3.240964{col 27}{space 2} .0561568{col 38}{space 1}   57.71{col 47}{space 3}0.000{col 55}{space 4} 3.130621{col 68}{space 3} 3.351307
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revdeploysoldiers ib3.militarytxt, robust

{txt}Linear regression                               Number of obs     = {res}       485
                                                {txt}F(2, 482)         =  {res}     5.66
                                                {txt}Prob > F          = {res}    0.0037
                                                {txt}R-squared         = {res}    0.0225
                                                {txt}Root MSE          =    {res} .75934

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeployso~s{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} -.042328{col 27}{space 2} .0848468{col 38}{space 1}   -0.50{col 47}{space 3}0.618{col 55}{space 4}-.2090434{col 68}{space 3} .1243873
{txt}Save soldi..  {c |}{col 15}{res}{space 2}  .222427{col 27}{space 2} .0827534{col 38}{space 1}    2.69{col 47}{space 3}0.007{col 55}{space 4} .0598251{col 68}{space 3} .3850289
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 3.190476{col 27}{space 2} .0578605{col 38}{space 1}   55.14{col 47}{space 3}0.000{col 55}{space 4} 3.076786{col 68}{space 3} 3.304166
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(2, 483)         =  {res}    10.62
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0421
                                                {txt}Root MSE          =    {res} .56644

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .1351411{col 27}{space 2} .0638279{col 38}{space 1}    2.12{col 47}{space 3}0.035{col 55}{space 4} .0097265{col 68}{space 3} .2605557
{txt}Save soldi..  {c |}{col 15}{res}{space 2}  .290293{col 27}{space 2} .0632207{col 38}{space 1}    4.59{col 47}{space 3}0.000{col 55}{space 4} .1660714{col 68}{space 3} .4145147
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 3.259921{col 27}{space 2} .0464572{col 38}{space 1}   70.17{col 47}{space 3}0.000{col 55}{space 4} 3.168637{col 68}{space 3} 3.351204
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Results are also robust to ordered-logit specification.
. ologit revdrones ib3.militarytxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-401.26106}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-393.40516}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-393.38624}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-393.38624}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:485}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:15.29}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0005}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-393.38624}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0196}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}    revdrones{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .4780678{col 27}{space 2} .2198372{col 38}{space 1}    2.17{col 47}{space 3}0.030{col 55}{space 4} .0471949{col 68}{space 3} .9089407
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .8938598{col 27}{space 2} .2293861{col 38}{space 1}    3.90{col 47}{space 3}0.000{col 55}{space 4} .4442712{col 68}{space 3} 1.343448
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-4.406241{col 27}{space 2} .5311896{col 55}{space 4}-5.447354{col 68}{space 3}-3.365129
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-2.865221{col 27}{space 2} .2763101{col 55}{space 4}-3.406779{col 68}{space 3}-2.323664
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} .1368225{col 27}{space 2} .1573829{col 55}{space 4}-.1716423{col 68}{space 3} .4452873
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit revpilots ib3.militarytxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-439.38585}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-423.74901}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-423.68109}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-423.68105}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:486}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:30.97}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-423.68105}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0357}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}    revpilots{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .9998001{col 27}{space 2} .2267864{col 38}{space 1}    4.41{col 47}{space 3}0.000{col 55}{space 4} .5553068{col 68}{space 3} 1.444293
{txt}Save soldi..  {c |}{col 15}{res}{space 2} 1.093829{col 27}{space 2} .2159711{col 38}{space 1}    5.06{col 47}{space 3}0.000{col 55}{space 4}  .670533{col 68}{space 3} 1.517124
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-1.832991{col 27}{space 2} .1932681{col 55}{space 4}-2.211789{col 68}{space 3}-1.454192
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} .5007713{col 27}{space 2} .1548738{col 55}{space 4} .1972241{col 68}{space 3} .8043184
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit revshelling ib3.militarytxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -477.1869}  
Iteration 1:{space 2}Log pseudolikelihood = {res: -469.4133}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-469.39655}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-469.39655}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:484}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:14.62}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0007}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-469.39655}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0163}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}  revshelling{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .2051782{col 27}{space 2} .2052467{col 38}{space 1}    1.00{col 47}{space 3}0.317{col 55}{space 4}-.1970979{col 68}{space 3} .6074544
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .8301557{col 27}{space 2} .2222432{col 38}{space 1}    3.74{col 47}{space 3}0.000{col 55}{space 4}  .394567{col 68}{space 3} 1.265744
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-1.573424{col 27}{space 2} .1716515{col 55}{space 4}-1.909855{col 68}{space 3}-1.236993
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} .3564985{col 27}{space 2} .1499018{col 55}{space 4} .0626964{col 68}{space 3} .6503006
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit revdeploysoldiers ib3.militarytxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-522.32887}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-516.18928}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-516.18085}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-516.18085}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:485}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:11.51}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0032}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-516.18085}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0118}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeployso~s{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2}-.0855888{col 27}{space 2} .2018504{col 38}{space 1}   -0.42{col 47}{space 3}0.672{col 55}{space 4}-.4812084{col 68}{space 3} .3100308
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .6074163{col 27}{space 2} .2126877{col 38}{space 1}    2.86{col 47}{space 3}0.004{col 55}{space 4} .1905562{col 68}{space 3} 1.024276
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-4.440743{col 27}{space 2} .4636104{col 55}{space 4}-5.349403{col 68}{space 3}-3.532083
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-1.386911{col 27}{space 2} .1552326{col 55}{space 4}-1.691161{col 68}{space 3} -1.08266
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2} .4193905{col 27}{space 2}  .142748{col 55}{space 4} .1396096{col 68}{space 3} .6991714
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit alphactoindex ib3.militarytxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-918.28635}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-907.22108}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-907.19869}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-907.19869}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:486}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:20.14}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-907.19869}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0121}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .4234464{col 27}{space 2} .1939068{col 38}{space 1}    2.18{col 47}{space 3}0.029{col 55}{space 4} .0433961{col 68}{space 3} .8034968
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .9593092{col 27}{space 2} .2139182{col 38}{space 1}    4.48{col 47}{space 3}0.000{col 55}{space 4} .5400373{col 68}{space 3} 1.378581
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2} -5.11153{col 27}{space 2}   .72637{col 55}{space 4}-6.535189{col 68}{space 3}-3.687871
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-3.847945{col 27}{space 2} .3852519{col 55}{space 4}-4.603025{col 68}{space 3}-3.092865
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-3.067705{col 27}{space 2} .2733061{col 55}{space 4}-3.603375{col 68}{space 3}-2.532035
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-2.878051{col 27}{space 2} .2550889{col 55}{space 4}-3.378016{col 68}{space 3}-2.378086
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2}-1.567127{col 27}{space 2} .1718826{col 55}{space 4}-1.904011{col 68}{space 3}-1.230244
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2}-.9440474{col 27}{space 2} .1589326{col 55}{space 4} -1.25555{col 68}{space 3}-.6325452
{txt}{space 8}/cut7 {c |}{col 15}{res}{space 2}-.1354484{col 27}{space 2} .1523006{col 55}{space 4} -.433952{col 68}{space 3} .1630552
{txt}{space 8}/cut8 {c |}{col 15}{res}{space 2} .2797942{col 27}{space 2} .1533301{col 55}{space 4}-.0207273{col 68}{space 3} .5803157
{txt}{space 8}/cut9 {c |}{col 15}{res}{space 2} .7408679{col 27}{space 2} .1577078{col 55}{space 4} .4317664{col 68}{space 3} 1.049969
{txt}{space 7}/cut10 {c |}{col 15}{res}{space 2} 1.015811{col 27}{space 2} .1588787{col 55}{space 4} .7044149{col 68}{space 3} 1.327208
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Support for Drones, Aerial Attacks, Artillery Assaults (paired T-tests)
. ttest revdrones = revdeploysoldiers if mosul==0

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
revdro~s {c |}{res}{col 12}    206{col 22} 3.024272{col 34} .0318647{col 46} .4573446{col 58} 2.961447{col 70} 3.087096
{txt}revdep~s {c |}{res}{col 12}    206{col 22} 2.718447{col 34} .0465911{col 46} .6687079{col 58} 2.626587{col 70} 2.810306
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    206{col 22} .3058252{col 34} .0466071{col 46}  .668938{col 58} .2139345{col 70}  .397716
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}revdrones{txt} - {res}revdeploysoldi~s{txt})              t = {res}  6.5618
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     205

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest revpilots = revdeploysoldiers if mosul==0

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
revpil~s {c |}{res}{col 12}    207{col 22} 2.961353{col 34} .0386473{col 46} .5560384{col 58} 2.885158{col 70} 3.037548
{txt}revdep~s {c |}{res}{col 12}    207{col 22} 2.719807{col 34} .0463854{col 46} .6673698{col 58} 2.628356{col 70} 2.811258
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    207{col 22} .2415459{col 34} .0576909{col 46} .8300272{col 58} .1278056{col 70} .3552862
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}revpilots{txt} - {res}revdeploysoldi~s{txt})              t = {res}  4.1869
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     206

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest revshelling = revdeploysoldiers if mosul==0

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
revshe~g {c |}{res}{col 12}    207{col 22} 2.777778{col 34} .0374413{col 46} .5386867{col 58}  2.70396{col 70} 2.851595
{txt}revdep~s {c |}{res}{col 12}    207{col 22} 2.719807{col 34} .0463854{col 46} .6673698{col 58} 2.628356{col 70} 2.811258
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    207{col 22}  .057971{col 34} .0520001{col 46} .7481509{col 58}-.0445496{col 70} .1604916
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}revshelling{txt} - {res}revdeploysoldi~s{txt})            t = {res}  1.1148
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     206

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}0.8669         {txt}Pr(|T| > |t|) = {res}0.2662          {txt}Pr(T > t) = {res}0.1331
{txt}
{com}. 
. ttest revdrones = revdeploysoldiers if mosul==1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
revdro~s {c |}{res}{col 12}    278{col 22} 3.910072{col 34} .0212435{col 46} .3542002{col 58} 3.868253{col 70} 3.951891
{txt}revdep~s {c |}{res}{col 12}    278{col 22} 3.640288{col 34}   .03457{col 46} .5763973{col 58} 3.572234{col 70} 3.708341
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    278{col 22} .2697842{col 34} .0405732{col 46} .6764907{col 58} .1899132{col 70} .3496552
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}revdrones{txt} - {res}revdeploysoldi~s{txt})              t = {res}  6.6493
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     277

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest revpilots = revdeploysoldiers if mosul==1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
revpil~s {c |}{res}{col 12}    278{col 22} 3.838129{col 34} .0243653{col 46}   .40625{col 58} 3.790165{col 70} 3.886094
{txt}revdep~s {c |}{res}{col 12}    278{col 22} 3.640288{col 34}   .03457{col 46} .5763973{col 58} 3.572234{col 70} 3.708341
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    278{col 22} .1978417{col 34} .0367707{col 46} .6130904{col 58} .1254562{col 70} .2702273
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}revpilots{txt} - {res}revdeploysoldi~s{txt})              t = {res}  5.3804
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     277

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. ttest revshelling = revdeploysoldiers if mosul==1

{txt}Paired t test
{hline 9}{c TT}{hline 68}
Variable{col 10}{c |}{col 16}Obs{col 27}Mean{col 35}Std. err.{col 47}Std. dev.{col 59}[95% conf. interval]
{hline 9}{c +}{hline 68}
revshe~g {c |}{res}{col 12}    276{col 22} 3.800725{col 34} .0271724{col 46} .4514214{col 58} 3.747232{col 70} 3.854217
{txt}revdep~s {c |}{res}{col 12}    276{col 22} 3.637681{col 34} .0347716{col 46} .5776697{col 58} 3.569229{col 70} 3.706134
{txt}{hline 9}{c +}{hline 68}
    diff {c |}{res}{col 12}    276{col 22} .1630435{col 34} .0302957{col 46} .5033092{col 58} .1034026{col 70} .2226843
{txt}{hline 9}{c BT}{hline 68}
     mean(diff) = mean({res}revshelling{txt} - {res}revdeploysoldi~s{txt})            t = {res}  5.3817
{txt} H0: mean(diff) = 0                              Degrees of freedom = {res}     275

 {txt}Ha: mean(diff) < 0           Ha: mean(diff) != 0           Ha: mean(diff) > 0
 Pr(T < t) = {res}1.0000         {txt}Pr(|T| > |t|) = {res}0.0000          {txt}Pr(T > t) = {res}0.0000
{txt}
{com}. 
. *Experimental Balance tests
. 
. iebaltab female age education unemployed sunni arab addisisvictim3 mosul revtrustarmy, groupvar(militarytxt0) vce(cluster location) savexlsx(balancepooled)

{res}{phang}Balance table saved in Excel format to: {browse "balancepooled.xlsx":balancepooled.xlsx}{p_end}
{txt}
{com}. 
. *Treatment Effect Estimation Adjusted for Imbalances (Sacrificing Civilians vs. Control)
. *DV=CTO/COIN Support
. 
. teffects ipw (alphactoindex ) (civiliantxt  female, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 3.440e-18}  
Iteration 1:{space 2}EE criterion = {res: 3.558e-33}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       330
{txt:Estimator}{col 16}:{res: inverse-probability weights}
{txt:Outcome model}{col 16}:{res: weighted mean}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}alphactoin~x{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 1}civiliantxt {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}  .198413{col 26}{space 2} .0603961{col 37}{space 1}    3.29{col 46}{space 3}0.001{col 54}{space 4} .0800388{col 67}{space 3} .3167873
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 1}civiliantxt {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 3.218641{col 26}{space 2} .0433622{col 37}{space 1}   74.23{col 46}{space 3}0.000{col 54}{space 4} 3.133653{col 67}{space 3}  3.30363
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects aipw (alphactoindex female) (civiliantxt female, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 3.440e-18}  
Iteration 1:{space 2}EE criterion = {res: 1.642e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       330
{txt:Estimator}{col 16}:{res: augmented IPW}
{txt:Outcome model}{col 16}:{res: linear by ML}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}alphactoin~x{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 1}civiliantxt {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}  .198413{col 26}{space 2} .0603961{col 37}{space 1}    3.29{col 46}{space 3}0.001{col 54}{space 4} .0800388{col 67}{space 3} .3167873
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 1}civiliantxt {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 3.218641{col 26}{space 2} .0433622{col 37}{space 1}   74.23{col 46}{space 3}0.000{col 54}{space 4} 3.133653{col 67}{space 3}  3.30363
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects ipwra (alphactoindex female, linear ) (civiliantxt female, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 3.440e-18}  
Iteration 1:{space 2}EE criterion = {res: 6.222e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       330
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}alphactoin~x{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 1}civiliantxt {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}  .198413{col 26}{space 2} .0603961{col 37}{space 1}    3.29{col 46}{space 3}0.001{col 54}{space 4} .0800388{col 67}{space 3} .3167873
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 1}civiliantxt {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 3.218641{col 26}{space 2} .0433622{col 37}{space 1}   74.23{col 46}{space 3}0.000{col 54}{space 4} 3.133653{col 67}{space 3}  3.30363
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects psmatch (alphactoindex) (civiliantxt female, logit), vce(robust)
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       330
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}        26
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}        142
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}alphactoin~x{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 1}civiliantxt {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}  .198413{col 26}{space 2} .0605871{col 37}{space 1}    3.27{col 46}{space 3}0.001{col 54}{space 4} .0796645{col 67}{space 3} .3171616
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Treatment Effect Estimation Adjusted for Imbalances (Civilian vs Soldier Treatments)
. *DV = CTO/COIN Support 
. 
. teffects ipw (alphactoindex ) (civsoldiertxt  education, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 1.754e-24}  
Iteration 1:{space 2}EE criterion = {res: 4.408e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       318
{txt:Estimator}{col 16}:{res: inverse-probability weights}
{txt:Outcome model}{col 16}:{res: weighted mean}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE           {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2} .1617862{col 27}{space 2} .0616651{col 38}{space 1}    2.62{col 47}{space 3}0.009{col 55}{space 4} .0409249{col 68}{space 3} .2826475
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean        {txt}{c |}
civsoldiertxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} 3.386661{col 27}{space 2} .0443332{col 38}{space 1}   76.39{col 47}{space 3}0.000{col 55}{space 4} 3.299769{col 68}{space 3} 3.473552
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects aipw (alphactoindex education) (civsoldiertxt education, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 1.754e-24}  
Iteration 1:{space 2}EE criterion = {res: 2.304e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       318
{txt:Estimator}{col 16}:{res: augmented IPW}
{txt:Outcome model}{col 16}:{res: linear by ML}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE           {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2} .1619837{col 27}{space 2} .0615874{col 38}{space 1}    2.63{col 47}{space 3}0.009{col 55}{space 4} .0412747{col 68}{space 3} .2826928
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean        {txt}{c |}
civsoldiertxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} 3.386562{col 27}{space 2} .0442862{col 38}{space 1}   76.47{col 47}{space 3}0.000{col 55}{space 4} 3.299763{col 68}{space 3} 3.473362
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects ipwra (alphactoindex education, linear ) (civsoldiertxt education, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 1.754e-24}  
Iteration 1:{space 2}EE criterion = {res: 1.217e-31}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       318
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE           {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2} .1620414{col 27}{space 2} .0615718{col 38}{space 1}    2.63{col 47}{space 3}0.008{col 55}{space 4} .0413629{col 68}{space 3} .2827198
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean        {txt}{c |}
civsoldiertxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} 3.386548{col 27}{space 2} .0442785{col 38}{space 1}   76.48{col 47}{space 3}0.000{col 55}{space 4} 3.299764{col 68}{space 3} 3.473333
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects psmatch (alphactoindex) (civsoldiertxt education, logit), vce(robust)
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       318
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         2
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}        104
{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}   AI robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE           {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2} .1100259{col 27}{space 2} .0608484{col 38}{space 1}    1.81{col 47}{space 3}0.071{col 55}{space 4}-.0092349{col 68}{space 3} .2292866
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Treatment Effect Estimation Adjusted for Imbalances (Civilian vs Soldier Treatments)
. *DV = Tolerance of Civilian Casualties
. 
. teffects ipw (revdeathsacceptable ) (civsoldiertxt  education, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 1.054e-24}  
Iteration 1:{space 2}EE criterion = {res: 9.557e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       316
{txt:Estimator}{col 16}:{res: inverse-probability weights}
{txt:Outcome model}{col 16}:{res: weighted mean}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE           {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2} 1.712187{col 27}{space 2} .3183218{col 38}{space 1}    5.38{col 47}{space 3}0.000{col 55}{space 4} 1.088287{col 68}{space 3} 2.336086
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean        {txt}{c |}
civsoldiertxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} 6.193759{col 27}{space 2} .2338898{col 38}{space 1}   26.48{col 47}{space 3}0.000{col 55}{space 4} 5.735344{col 68}{space 3} 6.652175
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects aipw (revdeathsacceptable  education) (civsoldiertxt education, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 1.054e-24}  
Iteration 1:{space 2}EE criterion = {res: 5.997e-31}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       316
{txt:Estimator}{col 16}:{res: augmented IPW}
{txt:Outcome model}{col 16}:{res: linear by ML}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE           {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2}  1.71174{col 27}{space 2} .3182869{col 38}{space 1}    5.38{col 47}{space 3}0.000{col 55}{space 4} 1.087909{col 68}{space 3} 2.335571
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean        {txt}{c |}
civsoldiertxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} 6.193636{col 27}{space 2} .2338328{col 38}{space 1}   26.49{col 47}{space 3}0.000{col 55}{space 4} 5.735332{col 68}{space 3}  6.65194
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects ipwra (revdeathsacceptable  education, linear ) (civsoldiertxt education, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 1.056e-24}  
Iteration 1:{space 2}EE criterion = {res: 3.253e-31}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       316
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE           {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2} 1.712162{col 27}{space 2} .3181504{col 38}{space 1}    5.38{col 47}{space 3}0.000{col 55}{space 4} 1.088598{col 68}{space 3} 2.335725
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean        {txt}{c |}
civsoldiertxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} 6.193569{col 27}{space 2}  .233787{col 38}{space 1}   26.49{col 47}{space 3}0.000{col 55}{space 4} 5.735355{col 68}{space 3} 6.651783
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects psmatch (revdeathsacceptable ) (civsoldiertxt education, logit), vce(robust)
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       316
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         2
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}        102
{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}   AI robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE           {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2} 1.397162{col 27}{space 2} .3309854{col 38}{space 1}    4.22{col 47}{space 3}0.000{col 55}{space 4} .7484422{col 68}{space 3} 2.045881
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Treatment Effect Moderation
. 
. reg alphactoindex ib3.militarytxt##mosul, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(5, 480)         =  {res}   195.99
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6406
                                                {txt}Root MSE          =    {res} .34804

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .1411747{col 27}{space 2} .0613008{col 38}{space 1}    2.30{col 47}{space 3}0.022{col 55}{space 4} .0207237{col 68}{space 3} .2616257
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .0061223{col 27}{space 2} .0572982{col 38}{space 1}    0.11{col 47}{space 3}0.915{col 55}{space 4}-.1064641{col 68}{space 3} .1187086
{txt}{space 13} {c |}
{space 6}1.mosul {c |}{col 15}{res}{space 2} .9136888{col 27}{space 2} .0607603{col 38}{space 1}   15.04{col 47}{space 3}0.000{col 55}{space 4} .7942997{col 68}{space 3} 1.033078
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
{space 8}mosul {c |}
Sacrificin.. #{c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.0652456{col 27}{space 2} .0836567{col 38}{space 1}   -0.78{col 47}{space 3}0.436{col 55}{space 4}-.2296241{col 68}{space 3}  .099133
{txt}Save soldi.. #{c |}
{space 11}1  {c |}{col 15}{res}{space 2}  .103289{col 27}{space 2} .0785693{col 38}{space 1}    1.31{col 47}{space 3}0.189{col 55}{space 4}-.0510932{col 68}{space 3} .2576712
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 2.813953{col 27}{space 2} .0384037{col 38}{space 1}   73.27{col 47}{space 3}0.000{col 55}{space 4} 2.738493{col 68}{space 3} 2.889414
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt##c.revtrustarmy, robust

{txt}Linear regression                               Number of obs     = {res}       485
                                                {txt}F(5, 479)         =  {res}    61.41
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3551
                                                {txt}Root MSE          =    {res}  .4661

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2}-.5708963{col 27}{space 2} .2678215{col 38}{space 1}   -2.13{col 47}{space 3}0.034{col 55}{space 4}-1.097147{col 68}{space 3} -.044646
{txt}Save soldi..  {c |}{col 15}{res}{space 2}-.2781274{col 27}{space 2} .2146648{col 38}{space 1}   -1.30{col 47}{space 3}0.196{col 55}{space 4}-.6999285{col 68}{space 3} .1436737
{txt}{space 13} {c |}
{space 1}revtrustarmy {c |}{col 15}{res}{space 2} .4773603{col 27}{space 2} .0577228{col 38}{space 1}    8.27{col 47}{space 3}0.000{col 55}{space 4} .3639391{col 68}{space 3} .5907816
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
{space 11}c. {c |}
{space 1}revtrustarmy {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .2261629{col 27}{space 2} .0999045{col 38}{space 1}    2.26{col 47}{space 3}0.024{col 55}{space 4} .0298577{col 68}{space 3} .4224682
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .1742808{col 27}{space 2}  .082059{col 38}{space 1}    2.12{col 47}{space 3}0.034{col 55}{space 4} .0130406{col 68}{space 3}  .335521
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 2.055154{col 27}{space 2} .1439196{col 38}{space 1}   14.28{col 47}{space 3}0.000{col 55}{space 4} 1.772362{col 68}{space 3} 2.337946
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt##c.revalphaprotect, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(5, 480)         =  {res}    35.22
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.2561
                                                {txt}Root MSE          =    {res} .50074

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2}-1.267827{col 27}{space 2} .3716628{col 38}{space 1}   -3.41{col 47}{space 3}0.001{col 55}{space 4}-1.998114{col 68}{space 3}-.5375397
{txt}Save soldi..  {c |}{col 15}{res}{space 2}-.2067446{col 27}{space 2}  .444336{col 38}{space 1}   -0.47{col 47}{space 3}0.642{col 55}{space 4}-1.079829{col 68}{space 3} .6663394
{txt}{space 13} {c |}
revalphapro~t {c |}{col 15}{res}{space 2} .4153182{col 27}{space 2} .0662278{col 38}{space 1}    6.27{col 47}{space 3}0.000{col 55}{space 4}  .285186{col 68}{space 3} .5454504
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
{space 11}c. {c |}
revalphapro~t {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .4460047{col 27}{space 2} .1121096{col 38}{space 1}    3.98{col 47}{space 3}0.000{col 55}{space 4} .2257186{col 68}{space 3} .6662909
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .0975174{col 27}{space 2} .1271395{col 38}{space 1}    0.77{col 47}{space 3}0.443{col 55}{space 4}-.1523013{col 68}{space 3} .3473361
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 1.911314{col 27}{space 2} .2192231{col 38}{space 1}    8.72{col 47}{space 3}0.000{col 55}{space 4} 1.480558{col 68}{space 3} 2.342069
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt##c.addisisvictim3, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(5, 480)         =  {res}   107.01
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4988
                                                {txt}Root MSE          =    {res} .41102

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .1115782{col 27}{space 2} .0629013{col 38}{space 1}    1.77{col 47}{space 3}0.077{col 55}{space 4}-.0120177{col 68}{space 3}  .235174
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .0886967{col 27}{space 2} .0665963{col 38}{space 1}    1.33{col 47}{space 3}0.184{col 55}{space 4}-.0421596{col 68}{space 3} .2195531
{txt}{space 13} {c |}
addisisvict~3 {c |}{col 15}{res}{space 2} .3700712{col 27}{space 2} .0406997{col 38}{space 1}    9.09{col 47}{space 3}0.000{col 55}{space 4} .2900997{col 68}{space 3} .4500428
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
{space 11}c. {c |}
addisisvict~3 {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .0360214{col 27}{space 2} .0509322{col 38}{space 1}    0.71{col 47}{space 3}0.480{col 55}{space 4}-.0640562{col 68}{space 3} .1360991
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .0664491{col 27}{space 2} .0523614{col 38}{space 1}    1.27{col 47}{space 3}0.205{col 55}{space 4}-.0364368{col 68}{space 3} .1693351
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 2.907472{col 27}{space 2} .0416568{col 38}{space 1}   69.80{col 47}{space 3}0.000{col 55}{space 4}  2.82562{col 68}{space 3} 2.989324
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt##c.supportisis, robust

{txt}Linear regression                               Number of obs     = {res}       479
                                                {txt}F(5, 473)         =  {res}     7.50
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0673
                                                {txt}Root MSE          =    {res} .56219

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .0679949{col 27}{space 2} .4419353{col 38}{space 1}    0.15{col 47}{space 3}0.878{col 55}{space 4}-.8004043{col 68}{space 3} .9363942
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .6307558{col 27}{space 2} .3874319{col 38}{space 1}    1.63{col 47}{space 3}0.104{col 55}{space 4}-.1305448{col 68}{space 3} 1.392056
{txt}{space 13} {c |}
{space 2}supportisis {c |}{col 15}{res}{space 2} .2513092{col 27}{space 2} .0939991{col 38}{space 1}    2.67{col 47}{space 3}0.008{col 55}{space 4} .0666017{col 68}{space 3} .4360167
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
c.supportisis {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .0096727{col 27}{space 2} .1506354{col 38}{space 1}    0.06{col 47}{space 3}0.949{col 55}{space 4}-.2863246{col 68}{space 3}   .30567
{txt}Save soldi..  {c |}{col 15}{res}{space 2}-.1318226{col 27}{space 2} .1283756{col 38}{space 1}   -1.03{col 47}{space 3}0.305{col 55}{space 4}-.3840796{col 68}{space 3} .1204344
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 2.551514{col 27}{space 2} .2678511{col 38}{space 1}    9.53{col 47}{space 3}0.000{col 55}{space 4} 2.025189{col 68}{space 3} 3.077839
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Demographic Moderation (OLS Regression)
. reg alphactoindex ib3.militarytxt##female, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(5, 480)         =  {res}    13.73
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0758
                                                {txt}Root MSE          =    {res} .55813

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .1472102{col 27}{space 2} .0731518{col 38}{space 1}    2.01{col 47}{space 3}0.045{col 55}{space 4} .0034729{col 68}{space 3} .2909474
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .2240361{col 27}{space 2} .0693428{col 38}{space 1}    3.23{col 47}{space 3}0.001{col 55}{space 4} .0877833{col 68}{space 3}  .360289
{txt}{space 13} {c |}
{space 5}1.female {c |}{col 15}{res}{space 2}-.4136873{col 27}{space 2} .0863339{col 38}{space 1}   -4.79{col 47}{space 3}0.000{col 55}{space 4}-.5833263{col 68}{space 3}-.2440483
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
{space 7}female {c |}
Sacrificin.. #{c |}
{space 11}1  {c |}{col 15}{res}{space 2} .2011541{col 27}{space 2} .1268624{col 38}{space 1}    1.59{col 47}{space 3}0.113{col 55}{space 4}-.0481201{col 68}{space 3} .4504283
{txt}Save soldi.. #{c |}
{space 11}1  {c |}{col 15}{res}{space 2} .4282075{col 27}{space 2} .1539092{col 38}{space 1}    2.78{col 47}{space 3}0.006{col 55}{space 4} .1257885{col 68}{space 3} .7306264
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 3.323944{col 27}{space 2} .0518783{col 38}{space 1}   64.07{col 47}{space 3}0.000{col 55}{space 4} 3.222007{col 68}{space 3}  3.42588
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt##c.age, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(5, 480)         =  {res}    16.69
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0966
                                                {txt}Root MSE          =    {res} .55182

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .0785679{col 27}{space 2} .1788688{col 38}{space 1}    0.44{col 47}{space 3}0.661{col 55}{space 4}-.2728947{col 68}{space 3} .4300304
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .1496819{col 27}{space 2} .1514556{col 38}{space 1}    0.99{col 47}{space 3}0.324{col 55}{space 4}-.1479159{col 68}{space 3} .4472797
{txt}{space 13} {c |}
{space 10}age {c |}{col 15}{res}{space 2} .0093224{col 27}{space 2} .0031232{col 38}{space 1}    2.98{col 47}{space 3}0.003{col 55}{space 4} .0031855{col 68}{space 3} .0154593
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
{space 8}c.age {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .0023754{col 27}{space 2} .0049285{col 38}{space 1}    0.48{col 47}{space 3}0.630{col 55}{space 4}-.0073086{col 68}{space 3} .0120595
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .0034579{col 27}{space 2} .0038347{col 38}{space 1}    0.90{col 47}{space 3}0.368{col 55}{space 4}-.0040769{col 68}{space 3} .0109927
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}  2.96166{col 27}{space 2} .1168011{col 38}{space 1}   25.36{col 47}{space 3}0.000{col 55}{space 4} 2.732155{col 68}{space 3} 3.191164
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt##c.education, robust

{txt}Linear regression                               Number of obs     = {res}       485
                                                {txt}F(5, 479)         =  {res}     5.22
                                                {txt}Prob > F          = {res}    0.0001
                                                {txt}R-squared         = {res}    0.0514
                                                {txt}Root MSE          =    {res} .56604

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2}  .322982{col 27}{space 2} .2868343{col 38}{space 1}    1.13{col 47}{space 3}0.261{col 55}{space 4}-.2406271{col 68}{space 3} .8865911
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .0250218{col 27}{space 2} .3132659{col 38}{space 1}    0.08{col 47}{space 3}0.936{col 55}{space 4}-.5905234{col 68}{space 3}  .640567
{txt}{space 13} {c |}
{space 4}education {c |}{col 15}{res}{space 2}-.0593304{col 27}{space 2} .0590901{col 38}{space 1}   -1.00{col 47}{space 3}0.316{col 55}{space 4}-.1754382{col 68}{space 3} .0567775
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
{space 2}c.education {c |}
Sacrificin..  {c |}{col 15}{res}{space 2}-.0535941{col 27}{space 2} .0821673{col 38}{space 1}   -0.65{col 47}{space 3}0.515{col 55}{space 4}-.2150471{col 68}{space 3} .1078588
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .0763632{col 27}{space 2} .0872414{col 38}{space 1}    0.88{col 47}{space 3}0.382{col 55}{space 4}-.0950599{col 68}{space 3} .2477863
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}  3.46383{col 27}{space 2} .2103253{col 38}{space 1}   16.47{col 47}{space 3}0.000{col 55}{space 4} 3.050556{col 68}{space 3} 3.877104
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt##unemployed, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(5, 480)         =  {res}     4.49
                                                {txt}Prob > F          = {res}    0.0005
                                                {txt}R-squared         = {res}    0.0433
                                                {txt}Root MSE          =    {res} .56786

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .1481854{col 27}{space 2} .0686998{col 38}{space 1}    2.16{col 47}{space 3}0.032{col 55}{space 4} .0131959{col 68}{space 3} .2831749
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .3009411{col 27}{space 2} .0654789{col 38}{space 1}    4.60{col 47}{space 3}0.000{col 55}{space 4} .1722805{col 68}{space 3} .4296017
{txt}{space 13} {c |}
{space 1}1.unemployed {c |}{col 15}{res}{space 2} .1239316{col 27}{space 2} .2408151{col 38}{space 1}    0.51{col 47}{space 3}0.607{col 55}{space 4}-.3492504{col 68}{space 3} .5971137
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
{space 3}unemployed {c |}
Sacrificin.. #{c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.1481854{col 27}{space 2} .2586634{col 38}{space 1}   -0.57{col 47}{space 3}0.567{col 55}{space 4}-.6564379{col 68}{space 3} .3600672
{txt}Save soldi.. #{c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.1426077{col 27}{space 2} .2721866{col 38}{space 1}   -0.52{col 47}{space 3}0.601{col 55}{space 4}-.6774322{col 68}{space 3} .3922167
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 3.251068{col 27}{space 2} .0467051{col 38}{space 1}   69.61{col 47}{space 3}0.000{col 55}{space 4} 3.159297{col 68}{space 3}  3.34284
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt##sunni, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(5, 480)         =  {res}    68.93
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3024
                                                {txt}Root MSE          =    {res}  .4849

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2} .2361111{col 27}{space 2} .0968327{col 38}{space 1}    2.44{col 47}{space 3}0.015{col 55}{space 4} .0458428{col 68}{space 3} .4263795
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .1068841{col 27}{space 2} .0755103{col 38}{space 1}    1.42{col 47}{space 3}0.158{col 55}{space 4}-.0414875{col 68}{space 3} .2552557
{txt}{space 13} {c |}
{space 6}1.sunni {c |}{col 15}{res}{space 2} .7473118{col 27}{space 2} .0736899{col 38}{space 1}   10.14{col 47}{space 3}0.000{col 55}{space 4} .6025173{col 68}{space 3} .8921064
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
{space 8}sunni {c |}
Sacrificin.. #{c |}
{space 11}1  {c |}{col 15}{res}{space 2}-.1679468{col 27}{space 2} .1177151{col 38}{space 1}   -1.43{col 47}{space 3}0.154{col 55}{space 4}-.3992474{col 68}{space 3} .0633539
{txt}Save soldi.. #{c |}
{space 11}1  {c |}{col 15}{res}{space 2} .1147891{col 27}{space 2} .0987157{col 38}{space 1}    1.16{col 47}{space 3}0.245{col 55}{space 4}-.0791792{col 68}{space 3} .3087573
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 2.708333{col 27}{space 2} .0549369{col 38}{space 1}   49.30{col 47}{space 3}0.000{col 55}{space 4} 2.600387{col 68}{space 3}  2.81628
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg alphactoindex ib3.militarytxt##arab, robust

{txt}Linear regression                               Number of obs     = {res}       486
                                                {txt}F(5, 480)         =  {res}    15.68
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0662
                                                {txt}Root MSE          =    {res} .56103

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}militarytxt {c |}
Sacrificin..  {c |}{col 15}{res}{space 2}    1.125{col 27}{space 2} .1984756{col 38}{space 1}    5.67{col 47}{space 3}0.000{col 55}{space 4} .7350116{col 68}{space 3} 1.514988
{txt}Save soldi..  {c |}{col 15}{res}{space 2} .6428571{col 27}{space 2} .2763965{col 38}{space 1}    2.33{col 47}{space 3}0.020{col 55}{space 4} .0997605{col 68}{space 3} 1.185954
{txt}{space 13} {c |}
{space 7}1.arab {c |}{col 15}{res}{space 2} .5421941{col 27}{space 2} .1940072{col 38}{space 1}    2.79{col 47}{space 3}0.005{col 55}{space 4} .1609858{col 68}{space 3} .9234023
{txt}{space 13} {c |}
{space 2}militarytxt#{c |}
{space 9}arab {c |}
Sacrificin.. #{c |}
{space 11}1  {c |}{col 15}{res}{space 2}-1.034283{col 27}{space 2} .2087572{col 38}{space 1}   -4.95{col 47}{space 3}0.000{col 55}{space 4}-1.444473{col 68}{space 3}-.6240919
{txt}Save soldi.. #{c |}
{space 11}1  {c |}{col 15}{res}{space 2} -.377445{col 27}{space 2} .2837704{col 38}{space 1}   -1.33{col 47}{space 3}0.184{col 55}{space 4}-.9350307{col 68}{space 3} .1801408
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2}     2.75{col 27}{space 2} .1882485{col 38}{space 1}   14.61{col 47}{space 3}0.000{col 55}{space 4} 2.380107{col 68}{space 3} 3.119893
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. *Mediation Analysis
. 
. *Structural Equation Model
. 
. sem (revdeathsacceptable -> alphactoindex, ) (civsoldiertxt -> alphactoindex, ) (civsoldiertxt -> revdeathsacceptable, ), vce(robust) nocapslatent
{res}{txt}(178 observations with missing values excluded)

Endogenous variables
{p 2 12 2}Observed:{space 1}{res}revdeathsacceptable alphactoindex{p_end}
{txt}
Exogenous variables
{p 2 12 2}Observed:{space 1}{res}civsoldiertxt{p_end}
{txt}
Fitting target model:
Iteration 0:{space 2}Log pseudolikelihood = {res:-1171.2341}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-1171.2341}  

{col 1}Structural equation model{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:316}
{txt}{col 1}Estimation method: {res:ml}

{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-1171.2341}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Structural   {col 15}{txt}{c |}
{space 2}{col 3}revdeaths~e{col 15}{c |}
{space 2}civsoldie~t {c |}{col 15}{res}{space 2} 1.692721{col 27}{space 2} .3176714{col 38}{space 1}    5.33{col 47}{space 3}0.000{col 55}{space 4} 1.070096{col 68}{space 3} 2.315345
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.209877{col 27}{space 2} .2328862{col 38}{space 1}   26.66{col 47}{space 3}0.000{col 55}{space 4} 5.753428{col 68}{space 3} 6.666325
{space 2}{txt}{hline 12}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}alphactoi~x{col 15}{c |}
{space 2}revdeaths~e {c |}{col 15}{res}{space 2} .1271417{col 27}{space 2} .0078391{col 38}{space 1}   16.22{col 47}{space 3}0.000{col 55}{space 4} .1117774{col 68}{space 3}  .142506
{txt}{space 2}civsoldie~t {c |}{col 15}{res}{space 2}-.0512945{col 27}{space 2} .0454528{col 38}{space 1}   -1.13{col 47}{space 3}0.259{col 55}{space 4}-.1403803{col 68}{space 3} .0377914
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.605527{col 27}{space 2} .0694718{col 38}{space 1}   37.50{col 47}{space 3}0.000{col 55}{space 4} 2.469365{col 68}{space 3}  2.74169
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
var(e.revde~e){c |}{col 15}{res}{space 2} 7.982288{col 27}{space 2} .4518539{col 55}{space 4} 7.144032{col 68}{space 3} 8.918903
{txt}var(e.alpha~x){c |}{col 15}{res}{space 2} .1667855{col 27}{space 2} .0146205{col 55}{space 4} .1404564{col 68}{space 3} .1980502
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. estat teffects
{res}

{txt}Direct effects
{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Structural   {col 15}{txt}{c |}
{space 2}{col 3}revdeaths~e{col 15}{c |}
{space 2}civsoldie~t {c |}{col 15}{res}{space 2} 1.692721{col 27}{space 2} .3176714{col 38}{space 1}    5.33{col 47}{space 3}0.000{col 55}{space 4} 1.070096{col 68}{space 3} 2.315345
{space 2}{txt}{hline 12}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}alphactoi~x{col 15}{c |}
{space 2}revdeaths~e {c |}{col 15}{res}{space 2} .1271417{col 27}{space 2} .0078391{col 38}{space 1}   16.22{col 47}{space 3}0.000{col 55}{space 4} .1117774{col 68}{space 3}  .142506
{txt}{space 2}civsoldie~t {c |}{col 15}{res}{space 2}-.0512945{col 27}{space 2} .0454528{col 38}{space 1}   -1.13{col 47}{space 3}0.259{col 55}{space 4}-.1403803{col 68}{space 3} .0377914
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}


Indirect effects
{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Structural   {col 15}{txt}{c |}
{space 2}{col 3}revdeaths~e{col 15}{c |}
{space 2}civsoldie~t {c |}{col 15}{res}{space 2}        0{col 27}{txt}  (no path)
{space 2}{hline 12}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}alphactoi~x{col 15}{c |}
{space 2}revdeaths~e {c |}{col 15}{res}{space 2}        0{col 27}{txt}  (no path)
{space 2}civsoldie~t {c |}{col 15}{res}{space 2} .2152154{col 27}{space 2} .0410269{col 38}{space 1}    5.25{col 47}{space 3}0.000{col 55}{space 4} .1348041{col 68}{space 3} .2956267
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}


Total effects
{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}{res}Structural   {col 15}{txt}{c |}
{space 2}{col 3}revdeaths~e{col 15}{c |}
{space 2}civsoldie~t {c |}{col 15}{res}{space 2} 1.692721{col 27}{space 2} .3176714{col 38}{space 1}    5.33{col 47}{space 3}0.000{col 55}{space 4} 1.070096{col 68}{space 3} 2.315345
{space 2}{txt}{hline 12}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 2}{col 3}alphactoi~x{col 15}{c |}
{space 2}revdeaths~e {c |}{col 15}{res}{space 2} .1271417{col 27}{space 2} .0078391{col 38}{space 1}   16.22{col 47}{space 3}0.000{col 55}{space 4} .1117774{col 68}{space 3}  .142506
{txt}{space 2}civsoldie~t {c |}{col 15}{res}{space 2}  .163921{col 27}{space 2} .0612415{col 38}{space 1}    2.68{col 47}{space 3}0.007{col 55}{space 4} .0438899{col 68}{space 3} .2839521
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. medsem, indep(civsoldiertxt) med(revdeathsacceptable) dep(alphactoindex) stand rit zlc

{txt}  Significance testing of indirect effect (standardised)
{c TLC}{hline 74}{c TRC}
{bf:  Estimates}{dup 10: }{c |}{bf:     Delta}{dup 7: }{c |} {bf:    Sobel}{dup 7: }{c |}{bf:  Monte Carlo}
{c LT}{hline 74}{c RT}
{res}  Indirect effect{col 22}{c |}     0.196{col 40}{c |}     0.196{col 58}{c |}     0.197

  Std. Err.{col 22}{c |}     0.037{col 40}{c |}     0.038{col 58}{c |}     0.039

  z-value{col 22}{c |}     5.310{col 40}{c |}     5.192{col 58}{c |}     5.073

  p-value{col 22}{c |}     0.000{col 40}{c |}     0.000{col 58}{c |}     0.000

  Conf. Interval{col 22}{c |} 0.123 , 0.268{col 40}{c |} 0.122 , 0.269{col 58}{c |} 0.127 , 0.273
{txt}{c LT}{hline 74}{c RT}

  Baron and Kenny approach to testing mediation
{res}  STEP 1 - revdeathsacceptable:civsoldiertxt (X -> M) with B=0.287 and p=0.000
  STEP 2 - alphactoindex:revdeathsacceptable (M -> Y) with B=0.682 and p=0.000
  STEP 3 - alphactoindex:civsoldiertxt (X -> Y) with B=-0.047 and p=0.261
{txt}           As STEP 1, STEP 2 and the Sobel's test above are significant 
           and STEP 3 is not significant the mediation is complete!


  Zhao, Lynch & Chen's approach to testing mediation
{res}  STEP 1 - alphactoindex:civsoldiertxt (X -> Y) with B=-0.047 and p=0.261
{txt}           As the Monte Carlo test above is significant and STEP 1 is not
           significant you have indirect-only mediation (full mediation)!


  RIT  =   (Indirect effect / Total effect)
{res}           (0.196 / 0.149) = 1.313
{txt}           Meaning that about131 % of the effect of civsoldiertxt
           on alphactoindex is mediated by revdeathsacceptable!

{c BLC}{hline 74}{c BRC}
  Note: to read more about this package{stata "help medsem": help medsem}

{com}. 
. *Potential Outcomes
. mediate (alphactoindex , linear) (revdeathsacceptable  , linear) (civsoldiertxt), vce(robust) all aequations
{res}
{txt}Iteration 0:{space 2}EE criterion = {res: 3.596e-29}  
Iteration 1:{space 2}EE criterion = {res: 8.418e-30}  
{res}
{txt}{col 1}Causal mediation analysis{col 60}{lalign 13:Number of obs}{col 73} = {res}{ralign 3:316}

{txt}Outcome model:{col 20}Linear
Mediator model:{col 20}Linear
Mediator variable:{res}{col 20}revdeathsacceptable
{txt}Treatment type:{col 20}Binary
{res}{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmeans       {txt}{c |}
{space 9}Y0M0 {c |}{col 15}{res}{space 2} 3.395062{col 27}{space 2} .0436615{col 38}{space 1}   77.76{col 47}{space 3}0.000{col 55}{space 4} 3.309487{col 68}{space 3} 3.480637
{txt}{space 9}Y1M0 {c |}{col 15}{res}{space 2} 3.311818{col 27}{space 2} .0514685{col 38}{space 1}   64.35{col 47}{space 3}0.000{col 55}{space 4} 3.210941{col 68}{space 3} 3.412694
{txt}{space 9}Y0M1 {c |}{col 15}{res}{space 2} 3.585428{col 27}{space 2} .0401836{col 38}{space 1}   89.23{col 47}{space 3}0.000{col 55}{space 4}  3.50667{col 68}{space 3} 3.664186
{txt}{space 9}Y1M1 {c |}{col 15}{res}{space 2} 3.558983{col 27}{space 2}  .042838{col 38}{space 1}   83.08{col 47}{space 3}0.000{col 55}{space 4} 3.475022{col 68}{space 3} 3.642944
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}NIE           {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2} .2471649{col 27}{space 2} .0515774{col 38}{space 1}    4.79{col 47}{space 3}0.000{col 55}{space 4} .1460751{col 68}{space 3} .3482548
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}NDE           {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2} -.083244{col 27}{space 2} .0526635{col 38}{space 1}   -1.58{col 47}{space 3}0.114{col 55}{space 4}-.1864625{col 68}{space 3} .0199745
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}PNIE          {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2} .1903663{col 27}{space 2} .0375996{col 38}{space 1}    5.06{col 47}{space 3}0.000{col 55}{space 4} .1166724{col 68}{space 3} .2640602
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}TNDE          {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2}-.0264453{col 27}{space 2} .0438253{col 38}{space 1}   -0.60{col 47}{space 3}0.546{col 55}{space 4}-.1123413{col 68}{space 3} .0594506
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}TE            {txt}{c |}
civsoldiertxt {c |}
(Saving so..  {c |}
{space 10}vs  {c |}
Sacrifici..)  {c |}{col 15}{res}{space 2}  .163921{col 27}{space 2} .0611439{col 38}{space 1}    2.68{col 47}{space 3}0.007{col 55}{space 4} .0440811{col 68}{space 3} .2837608
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}alphactoindex {txt}{c |}
civsoldiertxt {c |}
Saving sol..  {c |}{col 15}{res}{space 2}-.2916142{col 27}{space 2} .1326117{col 38}{space 1}   -2.20{col 47}{space 3}0.028{col 55}{space 4}-.5515284{col 68}{space 3}   -.0317
{txt}revdeathsac~e {c |}{col 15}{res}{space 2} .1124617{col 27}{space 2} .0109582{col 38}{space 1}   10.26{col 47}{space 3}0.000{col 55}{space 4} .0909841{col 68}{space 3} .1339394
{txt}{space 13} {c |}
civsoldiertxt#{c |}
{space 11}c. {c |}
revdeathsac~e {c |}
Saving sol..  {c |}{col 15}{res}{space 2} .0335546{col 27}{space 2} .0154915{col 38}{space 1}    2.17{col 47}{space 3}0.030{col 55}{space 4} .0031918{col 68}{space 3} .0639175
{txt}{space 13} {c |}
{space 8}_cons {c |}{col 15}{res}{space 2} 2.696688{col 27}{space 2} .0884666{col 38}{space 1}   30.48{col 47}{space 3}0.000{col 55}{space 4} 2.523297{col 68}{space 3}  2.87008
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}revdeathsac~e {txt}{c |}
civsoldiertxt {c |}
Saving sol..  {c |}{col 15}{res}{space 2} 1.692721{col 27}{space 2} .3171683{col 38}{space 1}    5.34{col 47}{space 3}0.000{col 55}{space 4} 1.071082{col 68}{space 3} 2.314359
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.209877{col 27}{space 2} .2325174{col 38}{space 1}   26.71{col 47}{space 3}0.000{col 55}{space 4} 5.754151{col 68}{space 3} 6.665602
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{p}Note: Outcome equation includes treatment{ch_endash}mediator interaction.{p_end}

{com}. 
. *Hicks/Tingley 
. 
. medeff (regress revdeathsacceptable civsoldiertxt) (regress alphactoindex civsoldiertxt revdeathsacceptable ), treat(civsoldiertxt) mediate(revdeathsacceptable) vce(robust)
Using 0 and 1 as treatment values

{txt}Linear regression                               Number of obs     = {res}       316
                                                {txt}F(1, 314)         =  {res}    28.30
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0823
                                                {txt}Root MSE          =    {res} 2.8343

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2} 1.692721{col 27}{space 2} .3181768{col 38}{space 1}    5.32{col 47}{space 3}0.000{col 55}{space 4} 1.066693{col 68}{space 3} 2.318749
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.209877{col 27}{space 2} .2332567{col 38}{space 1}   26.62{col 47}{space 3}0.000{col 55}{space 4} 5.750933{col 68}{space 3}  6.66882
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}
{txt}Linear regression                               Number of obs     = {res}       316
                                                {txt}F(2, 313)         =  {res}   131.03
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.4487
                                                {txt}Root MSE          =    {res} .41035

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2}-.0512945{col 27}{space 2} .0455978{col 38}{space 1}   -1.12{col 47}{space 3}0.261{col 55}{space 4}-.1410114{col 68}{space 3} .0384224
{txt}revdeathsac~e {c |}{col 15}{res}{space 2} .1271417{col 27}{space 2} .0078641{col 38}{space 1}   16.17{col 47}{space 3}0.000{col 55}{space 4} .1116686{col 68}{space 3} .1426148
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.605527{col 27}{space 2} .0696934{col 38}{space 1}   37.39{col 47}{space 3}0.000{col 55}{space 4} 2.468401{col 68}{space 3} 2.742654
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}The number of observations in the data is less than the number of simulations. Expanding the data to the number of simulations
{res}{txt}{hline 31}{c TT}{hline 52}
        Effect                 {c |}  Mean           [95% Conf. Interval]
{hline 31}{c +}{hline 52}
{res}        ACME                   {c |}  .2175581      .1328353      .3086464
        Direct Effect          {c |} -.0512687     -.1420452      .0383042
        Total Effect           {c |}  .1662893      .0450946      .2920313
        % of Tot Eff mediated  {c |}  1.287122      .7436046      4.692705
{txt}{hline 31}{c BT}{hline 52}

{com}. 
. medsens (regress revdeathsacceptable civsoldiertxt) (regress alphactoindex civsoldiertxt revdeathsacceptable ), treat(civsoldiertxt) mediate(revdeathsacceptable)

{txt}      Source {c |}       SS           df       MS      Number of obs   ={res}       316
{txt}{hline 13}{c +}{hline 34}   F(1, 314)       = {res}    28.16
{txt}       Model {c |} {res}  226.21393         1   226.21393   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 2522.40316       314  8.03313108   {txt}R-squared       ={res}    0.0823
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.0794
{txt}       Total {c |} {res} 2748.61709       315  8.72576854   {txt}Root MSE        =   {res} 2.8343

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2} 1.692721{col 27}{space 2} .3189834{col 38}{space 1}    5.31{col 47}{space 3}0.000{col 55}{space 4} 1.065106{col 68}{space 3} 2.320336
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.209877{col 27}{space 2} .2226819{col 38}{space 1}   27.89{col 47}{space 3}0.000{col 55}{space 4} 5.771739{col 68}{space 3} 6.648014
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(178 missing values generated)

      Source {c |}       SS           df       MS      Number of obs   ={res}       316
{txt}{hline 13}{c +}{hline 34}   F(2, 313)       = {res}   127.38
{txt}       Model {c |} {res} 42.8960569         2  21.4480284   {txt}Prob > F        ={res}    0.0000
{txt}    Residual {c |} {res} 52.7042202       313   .16838409   {txt}R-squared       ={res}    0.4487
{txt}{hline 13}{c +}{hline 34}   Adj R-squared   ={res}    0.4452
{txt}       Total {c |} {res} 95.6002771       315  .303492943   {txt}Root MSE        =   {res} .41035

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 1}alphactoindex{col 15}{c |} Coefficient{col 27}  Std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
revdeathsac~e {c |}{col 15}{res}{space 2} .1271417{col 27}{space 2} .0081704{col 38}{space 1}   15.56{col 47}{space 3}0.000{col 55}{space 4} .1110659{col 68}{space 3} .1432176
{txt}civsoldiertxt {c |}{col 15}{res}{space 2}-.0512945{col 27}{space 2} .0482088{col 38}{space 1}   -1.06{col 47}{space 3}0.288{col 55}{space 4}-.1461487{col 68}{space 3} .0435598
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 2.605527{col 27}{space 2} .0601138{col 38}{space 1}   43.34{col 47}{space 3}0.000{col 55}{space 4} 2.487249{col 68}{space 3} 2.723806
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}(176 missing values generated)
{res}{txt}{p 0 4 2}
file {bf}
C:\Users\swhitt\AppData\Local\Temp\ST_a444_000002.tmp{rm}
saved
as .dta format
{p_end}


{hline 64}
Sensitivity results
{hline 41}{c TT}{hline 22}
{res}        Rho at which ACME = 0            {c |}     .6604
        R^2_M*R^2_Y* at which ACME = 0:  {c |}     .4361
        R^2_M~R^2_Y~ at which ACME = 0:  {c |}     .2206
{txt}{hline 41}{c BT}{hline 22}
95% Confidence interval

{com}. 
. *Sensitivity Analysis 
. 
. regsensitivity alphactoindex mosul ib3.militarytxt  female age education unemployed sunni arab, 
{res}{txt}(note: graph osterplot not found)
{res}{txt}(note: graph dmpplot not found)

{res}
{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: DMP (2022){col 48}{txt}Number of obs{col 67}{res}=         485
{col 48}{txt}Beta(short){col 67}{res}=       0.930
{txt}Treatment{col 18}{res}: mosul{col 48}{txt}Beta(medium){col 67}{res}=       0.819
{txt}Outcome{col 18}{res}: alphactoindex{col 48}{txt}R2(short){col 67}{res}=       0.634
{col 48}{txt}R2(medium){col 67}{res}=       0.665
{col 48}{txt}Var(Y){col 67}{res}=       0.334
{col 48}{txt}Var(X){col 67}{res}=       0.245
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.152

{txt}Hypothesis{col 18}{res}: Beta > 0         {col 48}{txt}Breakdown Point{col 67}{res}=        66.2%
{txt}Other Params{col 18}{res}: rybar = +inf, cbar = 1

{txt}{hline 80}
{col 2}rxbar   {col 35} Beta
{hline 80}
{res}{col 2}0.000   {col 35}{txt}[{res} 0.8187{txt}, {res} 0.8187{txt} ]
{col 2}{res}0.079   {col 35}{txt}[{res} 0.7655{txt}, {res} 0.8718{txt} ]
{col 2}{res}0.158   {col 35}{txt}[{res} 0.7107{txt}, {res} 0.9267{txt} ]
{col 2}{res}0.236   {col 35}{txt}[{res} 0.6523{txt}, {res} 0.9850{txt} ]
{col 2}{res}0.315   {col 35}{txt}[{res} 0.5878{txt}, {res} 1.0495{txt} ]
{col 2}{res}0.394   {col 35}{txt}[{res} 0.5132{txt}, {res} 1.1241{txt} ]
{col 2}{res}0.473   {col 35}{txt}[{res} 0.4219{txt}, {res} 1.2154{txt} ]
{col 2}{res}0.551   {col 35}{txt}[{res} 0.3001{txt}, {res} 1.3372{txt} ]
{col 2}{res}0.630   {col 35}{txt}[{res} 0.1133{txt}, {res} 1.5240{txt} ]
{col 2}{res}0.709   {col 35}{txt}[{res}-0.2736{txt}, {res} 1.9110{txt} ]
{col 2}{res}0.788   {col 35}{txt}[   {res}-inf{txt},    {res}+inf{txt} ]
{hline 80}
{res}
{txt}{ul:Regression Sensitivity Analysis, Breakdown Frontier}

Analysis{col 18}{res}: Oster (2019)
{txt}Treatment{col 18}{res}: mosul
{txt}Outcome{col 18}{res}: alphactoindex
{txt}Hypothesis{col 18}{res}: Beta != 0         

{hline 80}
{col 2}{txt}R-squared(long){col 37}Delta(Breakdown)
{hline 80}
{col 2}{res}0.864               {col 37}85.9 %
{col 2}0.964               {col 37}71.7 %
{col 2}1.000               {col 37}67.6 %
{hline 80}
{txt}
{com}. 
. regsensitivity bounds alphactoindex mosul ib3.militarytxt  female age education unemployed sunni arab, oster
{res}ndeltapoints = 21, ngrid = 200

{txt}{ul:Regression Sensitivity Analysis, Bounds}

Analysis{col 18}{res}: Oster (2019){col 48}{txt}Number of obs{col 67}{res}=         485
{col 48}{txt}Beta(short){col 67}{res}=       0.930
{txt}Treatment{col 18}{res}: mosul{col 48}{txt}Beta(medium){col 67}{res}=       0.819
{txt}Outcome{col 18}{res}: alphactoindex{col 48}{txt}R2(short){col 67}{res}=       0.634
{col 48}{txt}R2(medium){col 67}{res}=       0.665
{col 48}{txt}Var(Y){col 67}{res}=       0.334
{col 48}{txt}Var(X){col 67}{res}=       0.245
{col 48}{txt}Var(X_Residual){col 67}{res}=       0.152

{txt}Hypothesis{col 18}{res}: Beta != 0         {col 48}{txt}Breakdown Point{col 67}{res}=        67.6%
{txt}Other Params{col 18}{res}: R-squared(long) = 1

{txt}{hline 80}
{col 2}delta   {col 35} Beta
{hline 80}
{res}{col 2}-1.000  {col 35}{txt}{{res} 1.0912{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.900  {col 35}{txt}{{res} 1.0891{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.800  {col 35}{txt}{{res} 1.0866{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.700  {col 35}{txt}{{res} 1.0833{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.600  {col 35}{txt}{{res} 1.0790{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.500  {col 35}{txt}{{res} 1.0731{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.400  {col 35}{txt}{{res} 1.0645{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.300  {col 35}{txt}{{res} 1.0507{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.200  {col 35}{txt}{{res} 1.0246{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}-0.100  {col 35}{txt}{{res} 0.9603{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.000   {col 35}{txt}{{res} 0.8187{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.100   {col 35}{txt}{{res} 0.6843{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.200   {col 35}{txt}{{res} 0.5716{txt}, {res}      .{txt}, {res}      .{txt} }
{col 2}{res}0.300   {col 35}{txt}{{res} 0.4666{txt}, {res} 1.2098{txt}, {res} 1.4889{txt} }
{col 2}{res}0.400   {col 35}{txt}{{res} 0.3607{txt}, {res} 1.1744{txt}, {res} 1.6997{txt} }
{col 2}{res}0.500   {col 35}{txt}{{res} 0.2470{txt}, {res} 1.1587{txt}, {res} 1.9266{txt} }
{col 2}{res}0.600   {col 35}{txt}{{res} 0.1174{txt}, {res} 1.1493{txt}, {res} 2.2115{txt} }
{col 2}{res}0.700   {col 35}{txt}{{res}-0.0412{txt}, {res} 1.1431{txt}, {res} 2.6197{txt} }
{col 2}{res}0.800   {col 35}{txt}{{res}-0.2553{txt}, {res} 1.1386{txt}, {res} 3.3251{txt} }
{col 2}{res}0.900   {col 35}{txt}{{res}-0.6000{txt}, {res} 1.1353{txt}, {res} 5.1335{txt} }
{col 2}{res}1.000   {col 35}{txt}{{res}-1.5297{txt}, {res} 1.1326{txt}, {res}      .{txt} }
{hline 80}

{com}. 
. *Power Calculations
. 
. *Power  - CTO/COIN
. 
. sum alphactoindex if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
alphactoin~x {c |}{res}        278    3.794664     .333301   2.333333          4
{txt}
{com}. sum alphactoindex if mosul==0

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
alphactoin~x {c |}{res}        208    2.868189    .3736045       1.75       3.75
{txt}
{com}. power twomeans 3.79 2.87, sd1(0.33) sd2(0.37) n1(218) n2(208)
{res}
{p 0 2 2}{txt}Estimated power for a two-sample means test{p_end}{txt}Satterthwaite's t test assuming unequal variances
{txt}{txt}{bind:H0: m2 = m1}  {txt}versus  {bind:Ha: m2 != m1}

{txt}Study parameters:

{txt}{ralign 16:alpha = }{res}   0.0500
{txt}{ralign 16:N = }{res}      426
{txt}{ralign 16:N1 = }{res}      218
{txt}{ralign 16:N2 = }{res}      208
{txt}{ralign 16:N2/N1 = }{res}   0.9541
{txt}{ralign 16:delta = }{res}  -0.9200
{txt}{ralign 16:m1 = }{res}   3.7900
{txt}{ralign 16:m2 = }{res}   2.8700
{txt}{ralign 16:sd1 = }{res}   0.3300
{txt}{ralign 16:sd2 = }{res}   0.3700

{p}{txt}Estimated power:{p_end}

{txt}{ralign 16:power = }{res}   1.0000
{txt}
{com}. 
. *Power Casualties
. 
. sum revdeathsacceptable if mosul==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revdeathsa~e {c |}{res}        196    8.882653    1.889649          2         10
{txt}
{com}. sum revdeathsacceptable if mosul==0

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revdeathsa~e {c |}{res}        120    4.016667    1.560992          0          9
{txt}
{com}. power twomeans 8.88 4.02, sd1(1.89) sd2(1.56) n1(196) n2(120)
{res}
{p 0 2 2}{txt}Estimated power for a two-sample means test{p_end}{txt}Satterthwaite's t test assuming unequal variances
{txt}{txt}{bind:H0: m2 = m1}  {txt}versus  {bind:Ha: m2 != m1}

{txt}Study parameters:

{txt}{ralign 16:alpha = }{res}   0.0500
{txt}{ralign 16:N = }{res}      316
{txt}{ralign 16:N1 = }{res}      196
{txt}{ralign 16:N2 = }{res}      120
{txt}{ralign 16:N2/N1 = }{res}   0.6122
{txt}{ralign 16:delta = }{res}  -4.8600
{txt}{ralign 16:m1 = }{res}   8.8800
{txt}{ralign 16:m2 = }{res}   4.0200
{txt}{ralign 16:sd1 = }{res}   1.8900
{txt}{ralign 16:sd2 = }{res}   1.5600

{p}{txt}Estimated power:{p_end}

{txt}{ralign 16:power = }{res}   1.0000
{txt}
{com}. 
. *Experimental ATEs
. 
. oneway alphactoindex militarytxt, tabulate

   {txt}military {c |}    Summary of alpha index of cto
 experiment {c |}             operations
 txt groups {c |}        Mean   Std. dev.       Freq.
{hline 12}{c +}{hline 36}
  Sacrifici {c |}  {res} 3.3950617   .55708898         162
  {txt}Save sold {c |}  {res} 3.5502137   .53562006         156
  {txt}Control,  {c |}  {res} 3.2599206   .60208729         168
{txt}{hline 12}{c +}{hline 36}
      Total {c |}  {res} 3.3981481   .57757343         486

                        {txt}Analysis of variance
    Source              SS         df      MS            F     Prob > F
{hline 72}
Between groups     {res} 6.81882548      2   3.40941274     10.63     0.0000
{txt} Within groups     {res} 154.972842    483   .320854745
{txt}{hline 72}
    Total          {res} 161.791667    485   .333591066

{txt}Bartlett's equal-variances test: chi2({res}2{txt}) = {res}  2.3132{txt}    Prob>chi2 = {res}0.315
{txt}
{com}. power oneway 3.40 3.55 3.26, n1(162) n2(156) n3(168) varerror(0.32)
{res}
{p 0 2 2}{txt}Estimated power{txt} for one-way ANOVA{p_end}{txt}F test for group effect
{txt}{txt}{bind:H0: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

{txt}Study parameters:

{txt}{ralign 16:alpha = }{res}   0.0500
{txt}{ralign 16:N = }{res}      486
{txt}{ralign 16:Average N = }{res} 162.0000
{txt}{ralign 16:N1 = }{res}      162
{txt}{ralign 16:N2 = }{res}      156
{txt}{ralign 16:N3 = }{res}      168
{txt}{ralign 16:delta = }{res}   0.2091
{txt}{ralign 16:N_g = }{res}        3
{txt}{ralign 16:m1 = }{res}   3.4000
{txt}{ralign 16:m2 = }{res}   3.5500
{txt}{ralign 16:m3 = }{res}   3.2600
{txt}{ralign 16:Var_m = }{res}   0.0140
{txt}{ralign 16:Var_e = }{res}   0.3200

{p}{txt}Estimated power:{p_end}

{txt}{ralign 16:power = }{res}   0.9892
{txt}
{com}. 
. *twoway factorial
. 
. anova alphactoindex militarytxt##mosul

                         {txt}Number of obs = {res}       486    {txt}R-squared     ={res}  0.6406
                         {txt}Root MSE      =   {res} .348043    {txt}Adj R-squared ={res}  0.6369

                  {txt}Source {c |} Partial SS         df         MS        F    Prob>F
       {hline 18}{c +}{hline 52}
                   Model {c |} {res} 103.64736          5   20.729472    171.13  0.0000
                         {txt}{c |}
             militarytxt {c |} {res} .97271937          2   .48635968      4.02  0.0187
{txt}                   mosul {c |} {res} 96.297406          1   96.297406    794.97  0.0000
{txt}       militarytxt#mosul {c |} {res} .50577456          2   .25288728      2.09  0.1251
                         {txt}{c |}
                Residual {c |} {res} 58.144305        480   .12113397  
       {txt}{hline 18}{c +}{hline 52}
                   Total {c |} {res} 161.79167        485   .33359107  
{txt}
{com}. power twoway 3.8 3.84 3.72 \ 2.96 2.82 2.81, n(486)  varerror(0.12)
{res}
{p 0 2 2}{txt}Estimated power{txt} for two-way ANOVA{p_end}{txt}F test for row effect
{txt}{txt}{bind:H0: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

{txt}Study parameters:

{txt}{ralign 16:alpha = }{res}   0.0500
{txt}{ralign 16:N = }{res}      486
{txt}{ralign 16:N per cell = }{res}       81
{txt}{ralign 16:delta = }{res}   1.3327
{txt}{ralign 16:N_r = }{res}        2
{txt}{ralign 16:N_c = }{res}        3
{txt}{ralign 16: means = }{res}  <matrix>
{txt}{ralign 16:Var_r = }{res}   0.2131
{txt}{ralign 16:Var_e = }{res}   0.1200

{p}{txt}Estimated power:{p_end}

{txt}{ralign 16:power = }{res}   1.0000
{txt}
{com}. power twoway 3.8 3.84 3.72 \ 2.96 2.82 2.81, n(486) cellweights( 2 3 2 \ 2 1 2) showcellsizes varerror(0.12)
{res}
{p 0 2 2}{txt}Estimated power{txt} for two-way ANOVA{p_end}{txt}F test for row effect
{txt}{txt}{bind:H0: delta = 0}  {txt}versus  {bind:Ha: delta != 0}

{txt}Study parameters:

{txt}{ralign 16:alpha = }{res}   0.0500
{txt}{ralign 16:N = }{res}      486
{txt}{ralign 16:delta = }{res}   1.2643
{txt}{ralign 16:N_r = }{res}        2
{txt}{ralign 16:N_c = }{res}        3
{txt}{ralign 16: means = }{res}  <matrix>
{txt}{ralign 16:Var_r = }{res}   0.1918
{txt}{ralign 16:Var_e = }{res}   0.1200

{txt}Actual sample sizes:

{txt}{ralign 16:N = }{res}      480
{txt}{ralign 16:Average N = }{res}  80.0000
{txt}{ralign 16: Nij = }{res}  <matrix>

{txt}Study matrices:
{res}
{txt}{p 2 2 2}Cell sample sizes{p_end}

{space 2}{space 13}{c |}{space 1}{rcenter 31:columns}{space 1}
{space 2}{space 0}{ralign 12:}{space 1}{c |}{space 1}{ralign 9:1}{space 1}{space 1}{ralign 9:2}{space 1}{space 1}{ralign 9:3}{space 1}
{space 2}{hline 13}{c   +}{hline 11}{hline 11}{hline 10}
{space 2}{txt:{lalign 13:rows}}{c |}{space 11}{space 11}{space 10}
{space 2}{space 0}{ralign 12:1}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       80}}}{space 1}{space 1}{ralign 9:{res:{sf:      120}}}{space 1}{space 1}{ralign 9:{res:{sf:       80}}}{space 1}
{space 2}{space 0}{ralign 12:2}{space 1}{c |}{space 1}{ralign 9:{res:{sf:       80}}}{space 1}{space 1}{ralign 9:{res:{sf:       40}}}{space 1}{space 1}{ralign 9:{res:{sf:       80}}}{space 1}


{p}{txt}Estimated power:{p_end}

{txt}{ralign 16:power = }{res}   1.0000
{txt}
{com}. 
. *subgroup of Save Soldiers vs. Sacrificing Civilians 
. 
. sum revdeathsacceptable if militarytxt==1

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revdeathsa~e {c |}{res}        162    6.209877     2.96864          1         10
{txt}
{com}. sum revdeathsacceptable if militarytxt==2

{txt}    Variable {c |}        Obs        Mean    Std. dev.       Min        Max
{hline 13}{c +}{hline 57}
revdeathsa~e {c |}{res}        154    7.902597    2.685642          0         10
{txt}
{com}. power twomeans 6.21 7.90, sd1(2.69) sd2(2.96) n1(162) n2(154)
{res}
{p 0 2 2}{txt}Estimated power for a two-sample means test{p_end}{txt}Satterthwaite's t test assuming unequal variances
{txt}{txt}{bind:H0: m2 = m1}  {txt}versus  {bind:Ha: m2 != m1}

{txt}Study parameters:

{txt}{ralign 16:alpha = }{res}   0.0500
{txt}{ralign 16:N = }{res}      316
{txt}{ralign 16:N1 = }{res}      162
{txt}{ralign 16:N2 = }{res}      154
{txt}{ralign 16:N2/N1 = }{res}   0.9506
{txt}{ralign 16:delta = }{res}   1.6900
{txt}{ralign 16:m1 = }{res}   6.2100
{txt}{ralign 16:m2 = }{res}   7.9000
{txt}{ralign 16:sd1 = }{res}   2.6900
{txt}{ralign 16:sd2 = }{res}   2.9600

{p}{txt}Estimated power:{p_end}

{txt}{ralign 16:power = }{res}   0.9996
{txt}
{com}. 
. *Predictors of Casualty Tolerance
. 
. reg revdeathsacceptable civsoldiertxt, robust

{txt}Linear regression                               Number of obs     = {res}       316
                                                {txt}F(1, 314)         =  {res}    28.30
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.0823
                                                {txt}Root MSE          =    {res} 2.8343

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2} 1.692721{col 27}{space 2} .3181768{col 38}{space 1}    5.32{col 47}{space 3}0.000{col 55}{space 4} 1.066693{col 68}{space 3} 2.318749
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.209877{col 27}{space 2} .2332567{col 38}{space 1}   26.62{col 47}{space 3}0.000{col 55}{space 4} 5.750933{col 68}{space 3}  6.66882
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revdeathsacceptable civsoldiertxt mosul, robust

{txt}Linear regression                               Number of obs     = {res}       316
                                                {txt}F(2, 313)         =  {res}   470.77
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6550
                                                {txt}Root MSE          =    {res} 1.7406

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2} .7097325{col 27}{space 2} .2215522{col 38}{space 1}    3.20{col 47}{space 3}0.001{col 55}{space 4} .2738125{col 68}{space 3} 1.145652
{txt}{space 8}mosul {c |}{col 15}{res}{space 2} 4.708831{col 27}{space 2} .2209392{col 38}{space 1}   21.31{col 47}{space 3}0.000{col 55}{space 4} 4.274118{col 68}{space 3} 5.143545
{txt}{space 8}_cons {c |}{col 15}{res}{space 2}  3.76826{col 27}{space 2} .1398516{col 38}{space 1}   26.94{col 47}{space 3}0.000{col 55}{space 4} 3.493092{col 68}{space 3} 4.043428
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revdeathsacceptable civsoldiertxt##c.revtrustarmy mosul, robust

{txt}Linear regression                               Number of obs     = {res}       315
                                                {txt}F(4, 310)         =  {res}   269.85
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6664
                                                {txt}Root MSE          =    {res} 1.7042

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}
Saving sol..  {c |}{col 15}{res}{space 2}-1.595219{col 27}{space 2} .8844592{col 38}{space 1}   -1.80{col 47}{space 3}0.072{col 55}{space 4}-3.335522{col 68}{space 3} .1450836
{txt}{space 1}revtrustarmy {c |}{col 15}{res}{space 2}-1.028026{col 27}{space 2} .2346083{col 38}{space 1}   -4.38{col 47}{space 3}0.000{col 55}{space 4}-1.489652{col 68}{space 3}-.5664001
{txt}{space 13} {c |}
civsoldiertxt#{c |}
{space 11}c. {c |}
{space 1}revtrustarmy {c |}
Saving sol..  {c |}{col 15}{res}{space 2} .8375501{col 27}{space 2} .3338971{col 38}{space 1}    2.51{col 47}{space 3}0.013{col 55}{space 4} .1805588{col 68}{space 3} 1.494541
{txt}{space 13} {c |}
{space 8}mosul {c |}{col 15}{res}{space 2} 5.012919{col 27}{space 2} .2383875{col 38}{space 1}   21.03{col 47}{space 3}0.000{col 55}{space 4} 4.543857{col 68}{space 3} 5.481981
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 6.402755{col 27}{space 2} .5946635{col 38}{space 1}   10.77{col 47}{space 3}0.000{col 55}{space 4} 5.232668{col 68}{space 3} 7.572842
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. reg revdeathsacceptable civsoldiertxt##c.revtrustarmy mosul supportisis revalphaprotect addisisvictim3 female age education unemployed sunni arab , robust

{txt}Linear regression                               Number of obs     = {res}       310
                                                {txt}F(13, 296)        =  {res}   103.24
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.6870
                                                {txt}Root MSE          =    {res} 1.6785

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}
Saving sol..  {c |}{col 15}{res}{space 2}-1.680749{col 27}{space 2} .8645425{col 38}{space 1}   -1.94{col 47}{space 3}0.053{col 55}{space 4}-3.382178{col 68}{space 3} .0206794
{txt}{space 1}revtrustarmy {c |}{col 15}{res}{space 2}-.9678776{col 27}{space 2} .2419441{col 38}{space 1}   -4.00{col 47}{space 3}0.000{col 55}{space 4}-1.444026{col 68}{space 3}-.4917289
{txt}{space 13} {c |}
civsoldiertxt#{c |}
{space 11}c. {c |}
{space 1}revtrustarmy {c |}
Saving sol..  {c |}{col 15}{res}{space 2} .8989126{col 27}{space 2} .3372035{col 38}{space 1}    2.67{col 47}{space 3}0.008{col 55}{space 4} .2352926{col 68}{space 3} 1.562533
{txt}{space 13} {c |}
{space 8}mosul {c |}{col 15}{res}{space 2} 5.897801{col 27}{space 2} .7609316{col 38}{space 1}    7.75{col 47}{space 3}0.000{col 55}{space 4} 4.400279{col 68}{space 3} 7.395322
{txt}{space 2}supportisis {c |}{col 15}{res}{space 2}-.1623206{col 27}{space 2} .2347871{col 38}{space 1}   -0.69{col 47}{space 3}0.490{col 55}{space 4}-.6243841{col 68}{space 3} .2997429
{txt}revalphapro~t {c |}{col 15}{res}{space 2} .0070828{col 27}{space 2} .2741875{col 38}{space 1}    0.03{col 47}{space 3}0.979{col 55}{space 4}-.5325211{col 68}{space 3} .5466867
{txt}addisisvict~3 {c |}{col 15}{res}{space 2} -.409403{col 27}{space 2} .3729686{col 38}{space 1}   -1.10{col 47}{space 3}0.273{col 55}{space 4}-1.143409{col 68}{space 3} .3246032
{txt}{space 7}female {c |}{col 15}{res}{space 2} .2894318{col 27}{space 2}  .246666{col 38}{space 1}    1.17{col 47}{space 3}0.242{col 55}{space 4}-.1960095{col 68}{space 3}  .774873
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0102365{col 27}{space 2}   .00828{col 38}{space 1}   -1.24{col 47}{space 3}0.217{col 55}{space 4}-.0265315{col 68}{space 3} .0060586
{txt}{space 4}education {c |}{col 15}{res}{space 2}-.3477909{col 27}{space 2} .1554747{col 38}{space 1}   -2.24{col 47}{space 3}0.026{col 55}{space 4}-.6537668{col 68}{space 3}-.0418149
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-1.143926{col 27}{space 2} .3874978{col 38}{space 1}   -2.95{col 47}{space 3}0.003{col 55}{space 4}-1.906525{col 68}{space 3}-.3813258
{txt}{space 8}sunni {c |}{col 15}{res}{space 2}-.5449761{col 27}{space 2} .2620505{col 38}{space 1}   -2.08{col 47}{space 3}0.038{col 55}{space 4}-1.060694{col 68}{space 3}-.0292578
{txt}{space 9}arab {c |}{col 15}{res}{space 2}-1.286049{col 27}{space 2} .3874934{col 38}{space 1}   -3.32{col 47}{space 3}0.001{col 55}{space 4} -2.04864{col 68}{space 3}-.5234579
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 9.904716{col 27}{space 2} 1.808623{col 38}{space 1}    5.48{col 47}{space 3}0.000{col 55}{space 4} 6.345326{col 68}{space 3} 13.46411
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. 
. ologit revdeathsacceptable civsoldiertxt, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -621.2097}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-609.94322}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-609.91232}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-609.91231}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:316}
{txt}{col 57}{lalign 13:Wald chi2({res:1})}{col 70} = {res}{ralign 6:20.92}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-609.91231}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.0182}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2} .9745277{col 27}{space 2} .2130826{col 38}{space 1}    4.57{col 47}{space 3}0.000{col 55}{space 4} .5568934{col 68}{space 3} 1.392162
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-5.392434{col 27}{space 2}  .997469{col 55}{space 4}-7.347438{col 68}{space 3}-3.437431
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-3.996699{col 27}{space 2} .5031817{col 55}{space 4}-4.982917{col 68}{space 3}-3.010481
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-2.637432{col 27}{space 2} .2662605{col 55}{space 4}-3.159293{col 68}{space 3}-2.115571
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.9973472{col 27}{space 2} .1609034{col 55}{space 4}-1.312712{col 68}{space 3}-.6819823
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2}-.4838638{col 27}{space 2} .1542965{col 55}{space 4}-.7862794{col 68}{space 3}-.1814482
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2}-.0937885{col 27}{space 2} .1550778{col 55}{space 4}-.3977355{col 68}{space 3} .2101585
{txt}{space 8}/cut7 {c |}{col 15}{res}{space 2} .1355958{col 27}{space 2} .1571444{col 55}{space 4}-.1724015{col 68}{space 3} .4435931
{txt}{space 8}/cut8 {c |}{col 15}{res}{space 2} .3175875{col 27}{space 2} .1580688{col 55}{space 4} .0077784{col 68}{space 3} .6273966
{txt}{space 8}/cut9 {c |}{col 15}{res}{space 2} .6102181{col 27}{space 2} .1657295{col 55}{space 4} .2853943{col 68}{space 3} .9350418
{txt}{space 7}/cut10 {c |}{col 15}{res}{space 2} 1.137125{col 27}{space 2} .1833069{col 55}{space 4} .7778497{col 68}{space 3} 1.496399
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit revdeathsacceptable civsoldiertxt mosul, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -621.2097}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-506.74876}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-494.82158}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-494.48049}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-494.47945}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-494.47945}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:316}
{txt}{col 57}{lalign 13:Wald chi2({res:2})}{col 70} = {res}{ralign 6:159.34}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-494.47945}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.2040}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}{col 15}{res}{space 2} .4785965{col 27}{space 2}  .230733{col 38}{space 1}    2.07{col 47}{space 3}0.038{col 55}{space 4} .0263681{col 68}{space 3}  .930825
{txt}{space 8}mosul {c |}{col 15}{res}{space 2} 4.228117{col 27}{space 2} .3798399{col 38}{space 1}   11.13{col 47}{space 3}0.000{col 55}{space 4} 3.483644{col 68}{space 3} 4.972589
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-4.657867{col 27}{space 2} 1.000364{col 55}{space 4}-6.618546{col 68}{space 3}-2.697189
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-3.248266{col 27}{space 2} .5074478{col 55}{space 4}-4.242845{col 68}{space 3}-2.253686
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-1.878843{col 27}{space 2} .2797269{col 55}{space 4}-2.427098{col 68}{space 3}-1.330589
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-.0011064{col 27}{space 2} .1654443{col 55}{space 4}-.3253712{col 68}{space 3} .3231584
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2} .8069073{col 27}{space 2} .1704369{col 55}{space 4} .4728572{col 68}{space 3} 1.140957
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2} 1.678856{col 27}{space 2} .1932821{col 55}{space 4} 1.300031{col 68}{space 3} 2.057682
{txt}{space 8}/cut7 {c |}{col 15}{res}{space 2} 2.295157{col 27}{space 2} .2666626{col 55}{space 4} 1.772508{col 68}{space 3} 2.817806
{txt}{space 8}/cut8 {c |}{col 15}{res}{space 2} 2.742808{col 27}{space 2} .2863085{col 55}{space 4} 2.181654{col 68}{space 3} 3.303962
{txt}{space 8}/cut9 {c |}{col 15}{res}{space 2} 3.377802{col 27}{space 2} .3127465{col 55}{space 4}  2.76483{col 68}{space 3} 3.990774
{txt}{space 7}/cut10 {c |}{col 15}{res}{space 2} 4.252233{col 27}{space 2} .3392361{col 55}{space 4} 3.587342{col 68}{space 3} 4.917123
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit revdeathsacceptable civsoldiertxt##c.revtrustarmy mosul, robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-614.45555}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-496.37179}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-483.03903}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-482.59081}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-482.58974}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-482.58974}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:315}
{txt}{col 57}{lalign 13:Wald chi2({res:4})}{col 70} = {res}{ralign 6:160.79}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-482.58974}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.2146}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}
Saving sol..  {c |}{col 15}{res}{space 2}-2.008429{col 27}{space 2} .8763173{col 38}{space 1}   -2.29{col 47}{space 3}0.022{col 55}{space 4} -3.72598{col 68}{space 3} -.290879
{txt}{space 1}revtrustarmy {c |}{col 15}{res}{space 2}-1.154755{col 27}{space 2} .2587128{col 38}{space 1}   -4.46{col 47}{space 3}0.000{col 55}{space 4}-1.661823{col 68}{space 3}-.6476875
{txt}{space 13} {c |}
civsoldiertxt#{c |}
{space 11}c. {c |}
{space 1}revtrustarmy {c |}
Saving sol..  {c |}{col 15}{res}{space 2} .9059685{col 27}{space 2} .3291561{col 38}{space 1}    2.75{col 47}{space 3}0.006{col 55}{space 4} .2608343{col 68}{space 3} 1.551103
{txt}{space 13} {c |}
{space 8}mosul {c |}{col 15}{res}{space 2} 4.713103{col 27}{space 2}  .444802{col 38}{space 1}   10.60{col 47}{space 3}0.000{col 55}{space 4} 3.841307{col 68}{space 3} 5.584899
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-6.538717{col 27}{space 2}  .803679{col 55}{space 4}-8.113899{col 68}{space 3}-4.963535
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} -4.94088{col 27}{space 2} .7284155{col 55}{space 4}-6.368548{col 68}{space 3}-3.513212
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-2.908111{col 27}{space 2} .6509515{col 55}{space 4}-4.183953{col 68}{space 3} -1.63227
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-2.042907{col 27}{space 2} .6289525{col 55}{space 4}-3.275631{col 68}{space 3}-.8101828
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2}-1.157795{col 27}{space 2} .6197896{col 55}{space 4} -2.37256{col 68}{space 3} .0569702
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2}-.5400535{col 27}{space 2} .6523148{col 55}{space 4}-1.818567{col 68}{space 3}   .73846
{txt}{space 8}/cut7 {c |}{col 15}{res}{space 2}-.0885393{col 27}{space 2} .6278008{col 55}{space 4}-1.319006{col 68}{space 3} 1.141928
{txt}{space 8}/cut8 {c |}{col 15}{res}{space 2} .5562348{col 27}{space 2} .6458509{col 55}{space 4}-.7096097{col 68}{space 3} 1.822079
{txt}{space 8}/cut9 {c |}{col 15}{res}{space 2} 1.442117{col 27}{space 2} .6617652{col 55}{space 4} .1450812{col 68}{space 3} 2.739153
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. ologit revdeathsacceptable civsoldiertxt##c.revtrustarmy mosul supportisis revalphaprotect addisisvictim3 female age education unemployed sunni arab , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res: -601.2958}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-477.43466}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-462.35139}  
Iteration 3:{space 2}Log pseudolikelihood = {res: -461.7809}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-461.77956}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-461.77956}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:310}
{txt}{col 57}{lalign 13:Wald chi2({res:13})}{col 70} = {res}{ralign 6:176.15}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-461.77956}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.2320}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1}revdeathsac~e{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
civsoldiertxt {c |}
Saving sol..  {c |}{col 15}{res}{space 2}-2.617986{col 27}{space 2} .8571314{col 38}{space 1}   -3.05{col 47}{space 3}0.002{col 55}{space 4}-4.297932{col 68}{space 3}-.9380391
{txt}{space 1}revtrustarmy {c |}{col 15}{res}{space 2}-1.114981{col 27}{space 2} .2847343{col 38}{space 1}   -3.92{col 47}{space 3}0.000{col 55}{space 4} -1.67305{col 68}{space 3} -.556912
{txt}{space 13} {c |}
civsoldiertxt#{c |}
{space 11}c. {c |}
{space 1}revtrustarmy {c |}
Saving sol..  {c |}{col 15}{res}{space 2} 1.101609{col 27}{space 2}  .332937{col 38}{space 1}    3.31{col 47}{space 3}0.001{col 55}{space 4} .4490649{col 68}{space 3} 1.754154
{txt}{space 13} {c |}
{space 8}mosul {c |}{col 15}{res}{space 2} 6.463021{col 27}{space 2} 1.056889{col 38}{space 1}    6.12{col 47}{space 3}0.000{col 55}{space 4} 4.391557{col 68}{space 3} 8.534485
{txt}{space 2}supportisis {c |}{col 15}{res}{space 2}-.1559539{col 27}{space 2} .2941859{col 38}{space 1}   -0.53{col 47}{space 3}0.596{col 55}{space 4}-.7325477{col 68}{space 3} .4206399
{txt}revalphapro~t {c |}{col 15}{res}{space 2} .3927818{col 27}{space 2} .3057966{col 38}{space 1}    1.28{col 47}{space 3}0.199{col 55}{space 4}-.2065685{col 68}{space 3} .9921321
{txt}addisisvict~3 {c |}{col 15}{res}{space 2}-.8644309{col 27}{space 2} .5095188{col 38}{space 1}   -1.70{col 47}{space 3}0.090{col 55}{space 4}-1.863069{col 68}{space 3} .1342077
{txt}{space 7}female {c |}{col 15}{res}{space 2} .2013456{col 27}{space 2} .2502525{col 38}{space 1}    0.80{col 47}{space 3}0.421{col 55}{space 4}-.2891403{col 68}{space 3} .6918315
{txt}{space 10}age {c |}{col 15}{res}{space 2}-.0119351{col 27}{space 2} .0083448{col 38}{space 1}   -1.43{col 47}{space 3}0.153{col 55}{space 4}-.0282906{col 68}{space 3} .0044204
{txt}{space 4}education {c |}{col 15}{res}{space 2}-.1983745{col 27}{space 2}  .182272{col 38}{space 1}   -1.09{col 47}{space 3}0.276{col 55}{space 4} -.555621{col 68}{space 3}  .158872
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2}-.9918593{col 27}{space 2} .3976922{col 38}{space 1}   -2.49{col 47}{space 3}0.013{col 55}{space 4}-1.771322{col 68}{space 3}-.2123969
{txt}{space 8}sunni {c |}{col 15}{res}{space 2}-.7354739{col 27}{space 2} .3015472{col 38}{space 1}   -2.44{col 47}{space 3}0.015{col 55}{space 4}-1.326496{col 68}{space 3}-.1444521
{txt}{space 9}arab {c |}{col 15}{res}{space 2}-1.483643{col 27}{space 2} .7052836{col 38}{space 1}   -2.10{col 47}{space 3}0.035{col 55}{space 4}-2.865974{col 68}{space 3}-.1013131
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2}-8.872862{col 27}{space 2} 2.037397{col 55}{space 4}-12.86609{col 68}{space 3}-4.879638
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2}-7.367141{col 27}{space 2} 1.943788{col 55}{space 4} -11.1769{col 68}{space 3}-3.557387
{txt}{space 8}/cut3 {c |}{col 15}{res}{space 2}-5.204227{col 27}{space 2} 1.936765{col 55}{space 4}-9.000216{col 68}{space 3}-1.408238
{txt}{space 8}/cut4 {c |}{col 15}{res}{space 2}-4.279683{col 27}{space 2} 1.942648{col 55}{space 4}-8.087202{col 68}{space 3} -.472164
{txt}{space 8}/cut5 {c |}{col 15}{res}{space 2}-3.333851{col 27}{space 2} 1.956263{col 55}{space 4}-7.168056{col 68}{space 3} .5003535
{txt}{space 8}/cut6 {c |}{col 15}{res}{space 2}-2.689679{col 27}{space 2} 1.980759{col 55}{space 4}-6.571896{col 68}{space 3} 1.192537
{txt}{space 8}/cut7 {c |}{col 15}{res}{space 2}-2.223714{col 27}{space 2} 1.961351{col 55}{space 4}-6.067891{col 68}{space 3} 1.620463
{txt}{space 8}/cut8 {c |}{col 15}{res}{space 2}-1.614803{col 27}{space 2} 1.959652{col 55}{space 4}-5.455651{col 68}{space 3} 2.226044
{txt}{space 8}/cut9 {c |}{col 15}{res}{space 2}-.7142594{col 27}{space 2} 1.990675{col 55}{space 4}-4.615911{col 68}{space 3} 3.187392
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Predictors of Military Trust
. 
. reg  revtrustarmy mosul supportisis revalphaprotect addisisvictim3 female age education unemployed sunni arab , robust

{txt}Linear regression                               Number of obs     = {res}       485
                                                {txt}F(10, 474)        =  {res}    27.99
                                                {txt}Prob > F          = {res}    0.0000
                                                {txt}R-squared         = {res}    0.3761
                                                {txt}Root MSE          =    {res} .43643

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} revtrustarmy{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      t{col 47}   P>|t|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}mosul {c |}{col 15}{res}{space 2} .6391032{col 27}{space 2}  .075499{col 38}{space 1}    8.47{col 47}{space 3}0.000{col 55}{space 4} .4907491{col 68}{space 3} .7874573
{txt}{space 2}supportisis {c |}{col 15}{res}{space 2} .0298426{col 27}{space 2} .0407208{col 38}{space 1}    0.73{col 47}{space 3}0.464{col 55}{space 4}-.0501731{col 68}{space 3} .1098582
{txt}revalphapro~t {c |}{col 15}{res}{space 2}  .035118{col 27}{space 2} .0580317{col 38}{space 1}    0.61{col 47}{space 3}0.545{col 55}{space 4}-.0789132{col 68}{space 3} .1491493
{txt}addisisvict~3 {c |}{col 15}{res}{space 2}-.0551844{col 27}{space 2} .0435628{col 38}{space 1}   -1.27{col 47}{space 3}0.206{col 55}{space 4}-.1407846{col 68}{space 3} .0304158
{txt}{space 7}female {c |}{col 15}{res}{space 2} .0845047{col 27}{space 2} .0557107{col 38}{space 1}    1.52{col 47}{space 3}0.130{col 55}{space 4}-.0249658{col 68}{space 3} .1939753
{txt}{space 10}age {c |}{col 15}{res}{space 2}  .002685{col 27}{space 2} .0013103{col 38}{space 1}    2.05{col 47}{space 3}0.041{col 55}{space 4} .0001102{col 68}{space 3} .0052597
{txt}{space 4}education {c |}{col 15}{res}{space 2}-.0014841{col 27}{space 2} .0250503{col 38}{space 1}   -0.06{col 47}{space 3}0.953{col 55}{space 4}-.0507075{col 68}{space 3} .0477394
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2} .0602199{col 27}{space 2} .0664037{col 38}{space 1}    0.91{col 47}{space 3}0.365{col 55}{space 4}-.0702621{col 68}{space 3} .1907019
{txt}{space 8}sunni {c |}{col 15}{res}{space 2} .1775262{col 27}{space 2}  .086167{col 38}{space 1}    2.06{col 47}{space 3}0.040{col 55}{space 4} .0082096{col 68}{space 3} .3468428
{txt}{space 9}arab {c |}{col 15}{res}{space 2} .2576929{col 27}{space 2} .1124797{col 38}{space 1}    2.29{col 47}{space 3}0.022{col 55}{space 4} .0366724{col 68}{space 3} .4787134
{txt}{space 8}_cons {c |}{col 15}{res}{space 2} 1.642193{col 27}{space 2} .2646794{col 38}{space 1}    6.20{col 47}{space 3}0.000{col 55}{space 4} 1.122103{col 68}{space 3} 2.162283
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}{txt}
{com}. ologit  revtrustarmy mosul supportisis revalphaprotect addisisvictim3 female age education unemployed sunni arab , robust

{res}{txt}Iteration 0:{space 2}Log pseudolikelihood = {res:-356.60902}  
Iteration 1:{space 2}Log pseudolikelihood = {res:-256.78131}  
Iteration 2:{space 2}Log pseudolikelihood = {res:-247.89112}  
Iteration 3:{space 2}Log pseudolikelihood = {res:-247.74878}  
Iteration 4:{space 2}Log pseudolikelihood = {res:-247.74872}  
Iteration 5:{space 2}Log pseudolikelihood = {res:-247.74872}  
{res}
{txt}{col 1}Ordered logistic regression{col 57}{lalign 13:Number of obs}{col 70} = {res}{ralign 6:485}
{txt}{col 57}{lalign 13:Wald chi2({res:10})}{col 70} = {res}{ralign 6:170.41}
{txt}{col 57}{lalign 13:Prob > chi2}{col 70} = {res}{ralign 6:0.0000}
{txt}{col 1}{lalign 20:Log pseudolikelihood}{col 21} = {res}{ralign 10:-247.74872}{txt}{col 57}{lalign 13:Pseudo R2}{col 70} = {res}{ralign 6:0.3053}

{txt}{hline 14}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 15}{c |}{col 27}    Robust
{col 1} revtrustarmy{col 15}{c |} Coefficient{col 27}  std. err.{col 39}      z{col 47}   P>|z|{col 55}     [95% con{col 68}f. interval]
{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}mosul {c |}{col 15}{res}{space 2} 3.671183{col 27}{space 2} .9474645{col 38}{space 1}    3.87{col 47}{space 3}0.000{col 55}{space 4} 1.814187{col 68}{space 3} 5.528179
{txt}{space 2}supportisis {c |}{col 15}{res}{space 2} .1395565{col 27}{space 2} .2829563{col 38}{space 1}    0.49{col 47}{space 3}0.622{col 55}{space 4}-.4150277{col 68}{space 3} .6941406
{txt}revalphapro~t {c |}{col 15}{res}{space 2} .0772695{col 27}{space 2} .2505507{col 38}{space 1}    0.31{col 47}{space 3}0.758{col 55}{space 4}-.4138009{col 68}{space 3} .5683398
{txt}addisisvict~3 {c |}{col 15}{res}{space 2}-.3364691{col 27}{space 2} .5307182{col 38}{space 1}   -0.63{col 47}{space 3}0.526{col 55}{space 4}-1.376658{col 68}{space 3} .7037195
{txt}{space 7}female {c |}{col 15}{res}{space 2}  .488524{col 27}{space 2}  .307554{col 38}{space 1}    1.59{col 47}{space 3}0.112{col 55}{space 4}-.1142707{col 68}{space 3} 1.091319
{txt}{space 10}age {c |}{col 15}{res}{space 2} .0297977{col 27}{space 2} .0139256{col 38}{space 1}    2.14{col 47}{space 3}0.032{col 55}{space 4}  .002504{col 68}{space 3} .0570915
{txt}{space 4}education {c |}{col 15}{res}{space 2} .0435803{col 27}{space 2} .1455276{col 38}{space 1}    0.30{col 47}{space 3}0.765{col 55}{space 4}-.2416485{col 68}{space 3} .3288092
{txt}{space 3}unemployed {c |}{col 15}{res}{space 2} .0421283{col 27}{space 2} .4128468{col 38}{space 1}    0.10{col 47}{space 3}0.919{col 55}{space 4}-.7670365{col 68}{space 3} .8512931
{txt}{space 8}sunni {c |}{col 15}{res}{space 2} .5089699{col 27}{space 2} .3021913{col 38}{space 1}    1.68{col 47}{space 3}0.092{col 55}{space 4}-.0833142{col 68}{space 3} 1.101254
{txt}{space 9}arab {c |}{col 15}{res}{space 2} 1.351665{col 27}{space 2} .5378308{col 38}{space 1}    2.51{col 47}{space 3}0.012{col 55}{space 4}  .297536{col 68}{space 3} 2.405794
{txt}{hline 14}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{space 8}/cut1 {c |}{col 15}{res}{space 2} .8023766{col 27}{space 2}  1.33439{col 55}{space 4} -1.81298{col 68}{space 3} 3.417733
{txt}{space 8}/cut2 {c |}{col 15}{res}{space 2} 4.034183{col 27}{space 2} 1.333198{col 55}{space 4} 1.421163{col 68}{space 3} 6.647203
{txt}{hline 14}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. 
. *Balance Tests – Mosul versus Basra
. 
. *Next, we show that the Mosul/Basra effect on CTO/COIN support is robust to adjustments for imbalances on gender, age, and Sunni religion
. 
. teffects ipw (alphactoindex) (mosul  female age sunni, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 5.926e-23}  
Iteration 1:{space 2}EE criterion = {res: 6.833e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       486
{txt:Estimator}{col 16}:{res: inverse-probability weights}
{txt:Outcome model}{col 16}:{res: weighted mean}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}alphactoin~x{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 7}mosul {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .5370773{col 26}{space 2} .1785605{col 37}{space 1}    3.01{col 46}{space 3}0.003{col 54}{space 4} .1871052{col 67}{space 3} .8870493
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 7}mosul {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 2.899969{col 26}{space 2} .0322577{col 37}{space 1}   89.90{col 46}{space 3}0.000{col 54}{space 4} 2.836745{col 67}{space 3} 2.963193
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects aipw (alphactoindex female age sunni) (mosul  female age sunni, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 5.926e-23}  
Iteration 1:{space 2}EE criterion = {res: 3.602e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       486
{txt:Estimator}{col 16}:{res: augmented IPW}
{txt:Outcome model}{col 16}:{res: linear by ML}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}alphactoin~x{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 7}mosul {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2}  .607319{col 26}{space 2}  .096991{col 37}{space 1}    6.26{col 46}{space 3}0.000{col 54}{space 4} .4172201{col 67}{space 3} .7974179
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 7}mosul {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 2.907342{col 26}{space 2} .0341781{col 37}{space 1}   85.06{col 46}{space 3}0.000{col 54}{space 4} 2.840354{col 67}{space 3}  2.97433
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects ipwra (alphactoindex female age sunni, linear ) (mosul  female age sunni, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 5.926e-23}  
Iteration 1:{space 2}EE criterion = {res: 2.572e-31}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       486
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}alphactoin~x{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 7}mosul {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .6841928{col 26}{space 2}  .062998{col 37}{space 1}   10.86{col 46}{space 3}0.000{col 54}{space 4} .5607191{col 67}{space 3} .8076665
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 7}mosul {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 2.902682{col 26}{space 2}  .034725{col 37}{space 1}   83.59{col 46}{space 3}0.000{col 54}{space 4} 2.834623{col 67}{space 3} 2.970742
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects psmatch (alphactoindex ) (mosul  female age sunni, logit), vce(robust)
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       486
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}         24
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}alphactoin~x{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 7}mosul {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} .8543652{col 26}{space 2} .0836748{col 37}{space 1}   10.21{col 46}{space 3}0.000{col 54}{space 4} .6903657{col 67}{space 3} 1.018365
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. *Next, we show that the Mosul/Basra effect on causality tolerance is robust to adjustments for imbalances on gender, age, and Sunni religion
. 
. teffects ipw (revdeathsacceptable) (mosul  female age sunni, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 2.932e-20}  
Iteration 1:{space 2}EE criterion = {res: 8.197e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       316
{txt:Estimator}{col 16}:{res: inverse-probability weights}
{txt:Outcome model}{col 16}:{res: weighted mean}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revdeathsa~e{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 7}mosul {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} 5.116063{col 26}{space 2} .1938105{col 37}{space 1}   26.40{col 46}{space 3}0.000{col 54}{space 4} 4.736201{col 67}{space 3} 5.495925
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 7}mosul {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 3.937531{col 26}{space 2} .1471657{col 37}{space 1}   26.76{col 46}{space 3}0.000{col 54}{space 4} 3.649092{col 67}{space 3} 4.225971
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects aipw (revdeathsacceptable female age sunni) (mosul  female age sunni, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 2.932e-20}  
Iteration 1:{space 2}EE criterion = {res: 6.171e-32}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       316
{txt:Estimator}{col 16}:{res: augmented IPW}
{txt:Outcome model}{col 16}:{res: linear by ML}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revdeathsa~e{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 7}mosul {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} 5.174716{col 26}{space 2} .1934168{col 37}{space 1}   26.75{col 46}{space 3}0.000{col 54}{space 4} 4.795626{col 67}{space 3} 5.553806
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 7}mosul {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 3.934976{col 26}{space 2} .1471838{col 37}{space 1}   26.74{col 46}{space 3}0.000{col 54}{space 4} 3.646501{col 67}{space 3} 4.223451
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects ipwra (revdeathsacceptable female age sunni, linear ) (mosul  female age sunni, logit), vce(robust)

{res}{txt}Iteration 0:{space 2}EE criterion = {res: 2.932e-20}  
Iteration 1:{space 2}EE criterion = {res: 2.556e-31}  
{res}
{txt}Treatment-effects estimation{col 49}Number of obs {col 67}= {res}       316
{txt:Estimator}{col 16}:{res: IPW regression adjustment}
{txt:Outcome model}{col 16}:{res: linear}
{txt:Treatment model}{col 16}:{res: logit}
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}    Robust
{col 1}revdeathsa~e{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 7}mosul {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} 5.162222{col 26}{space 2}  .192265{col 37}{space 1}   26.85{col 46}{space 3}0.000{col 54}{space 4}  4.78539{col 67}{space 3} 5.539055
{txt}{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}POmean       {txt}{c |}
{space 7}mosul {c |}
{space 10}0  {c |}{col 14}{res}{space 2} 3.940316{col 26}{space 2} .1448474{col 37}{space 1}   27.20{col 46}{space 3}0.000{col 54}{space 4}  3.65642{col 67}{space 3} 4.224212
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. teffects psmatch (revdeathsacceptable) (mosul  female age sunni, logit), vce(robust)
{res}
{txt}Treatment-effects estimation{col 48}Number of obs {col 67}= {res}       316
{txt:Estimator}{col 16}:{res: propensity-score matching}{col 48}{txt:Matches: requested }{col 67}{txt:=}          1
{txt:Outcome model}{col 16}:{res: matching}{txt}{col 63}min {col 67}= {res}         1
{txt:Treatment model}{col 16}:{res: logit}{col 63}{txt:max }{col 67}{txt:=}         17
{txt}{hline 13}{c TT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{col 14}{c |}{col 26}   AI robust
{col 1}revdeathsa~e{col 14}{c |} Coefficient{col 26}  std. err.{col 38}      z{col 46}   P>|z|{col 54}     [95% con{col 67}f. interval]
{hline 13}{c +}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}
{res}ATE          {txt}{c |}
{space 7}mosul {c |}
{space 3}(1 vs 0)  {c |}{col 14}{res}{space 2} 5.321981{col 26}{space 2} .2438686{col 37}{space 1}   21.82{col 46}{space 3}0.000{col 54}{space 4} 4.844007{col 67}{space 3} 5.799955
{txt}{hline 13}{c BT}{hline 11}{hline 11}{hline 9}{hline 8}{hline 13}{hline 12}

{com}. 
. log close
      {txt}name:  {res}<unnamed>
       {txt}log:  {res}C:\Users\swhitt\OneDrive - High Point University\Research\Mosul\Yazidi\Drones\TPV\Revision 1\Replication Instructions\TPV Soldiers Replication log file.smcl
  {txt}log type:  {res}smcl
 {txt}closed on:  {res}31 Oct 2025, 19:12:30
{txt}{.-}
{smcl}
{txt}{sf}{ul off}